Genetic polymorphisms associated with psoriasis, methdos of detection and uses thereof

ABSTRACT

The present invention is based on the discovery of genetic polymorphisms that are associated with psoriasis and related pathologies. In particular, the present invention relates to nucleic acid molecules containing the polymorphisms, including groups of nucleic acid molecules that may be used as a signature marker set, such as a haplotype, a diplotype, variant proteins encoded by such nucleic acid molecules, reagents for detecting the polymorphic nucleic acid molecules and proteins, and methods of using the nucleic acid and proteins as well as methods of using reagents for their detection.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. non-provisionalapplication Ser. No. 13/175,159, filed Jul. 1, 2011, which is adivisional application of U.S. non-provisional application Ser. No.11/899,017, filed Aug. 31, 2007 (issued as U.S. Pat. No. 7,993,833 onAug. 9, 2011), which claims priority to U.S. provisional applicationSer. No. 60/928,625, filed on May 9, 2007, and to U.S. provisionalapplication Ser. No. 60/844,100, filed on Sep. 11, 2006, the contentseach of which are hereby incorporated by reference in their entiretyinto this application.

FIELD OF THE INVENTION

The present invention is in the field of diagnosis and therapy ofpsoriasis. In particular, the present invention relates to specificsingle nucleotide polymorphisms (SNPs) in the human genome, and theirassociation with psoriasis and related pathologies. Based on differencesin allele frequencies in the psoriasis patient population relative tonormal individuals, the naturally-occurring SNPs disclosed herein can beused as targets for the design of diagnostic reagents and thedevelopment of therapeutic agents, as well as for disease associationand linkage analysis. In particular, the SNPs of the present inventionare useful for identifying an individual who is at an increased ordecreased risk of developing psoriasis and for early detection of thedisease, for providing clinically important information for theprevention and/or treatment of psoriasis, and for screening andselecting therapeutic agents. The SNPs disclosed herein are also usefulfor human identification applications. Methods, assays, kits, andreagents for detecting the presence of these polymorphisms and theirencoded products are provided.

BACKGROUND OF THE INVENTION

Psoriasis is a common, chronic, T-cell-mediated inflammatory disease ofthe skin affecting ˜2-3% of whites of European descent. Although thisdisease is found in all populations, its prevalence is lower in Asiansand African-Americans and also declines at lower latitudes.¹ The mostcommon form, psoriasis vulgaris, is characterized by varying numbers ofred, raised, scaly skin patches that can be present on any body surface,but most often appear on the elbows, knees and scalp. The onset ofdisease usually occurs early in life (15-30 years) and affects males andfemales equally. Up to 30% of individuals with psoriasis will develop aninflammatory arthritis, which can affect the peripheral joints of thehands and feet, large joints, or the central axial-skeleton.^(2,3)Pathologically, psoriasis is characterized by vascular changes,hyperproliferation of keratinocytes, altered epidermal differentiationand inflammation.⁴ In particular, the reaction of cells in the epidermisto type 1 effector molecules produced by T-cells results in thecharacteristic pathology of the plaques.⁵

The genetics of psoriasis are complex and highly heritable as evidencedby an increased rate of concordance in monozygotic twins over dizygotictwins (35%-72% vs. 12-23%) and a substantially increased incidence infamily members of affected individuals (first-degree relatives 6%);however, it is clear that environmental effects are also responsible fordisease susceptibility.⁵ Ten genome-wide linkage scans have resulted instrong evidence for a susceptibility locus in the MHC region on 6p21(PSORS1 [MIM 177900]), but have not yielded consistent evidence forother regions.⁵

Linkage and association in the MHC (6p21) are thought to be due toHLA-C, in particular psoriasis susceptibility effects are thought to becaused by the *0602 allele^(6,7), although other candidate genes in thearea may also contribute to disease predisposition. Association studieshave identified three genes under linkage peaks, with considerableevidence for linkage disequilibrium with psoriasis, namely SLC9A3R1/NAT9and RAPTOR (KIAA1303) in 17q25, and SLC12A8 in 3q21.^(8,9) Several othergenes including VDR, MMP2, IL10, IL1RN, IL12B, and IRF2 (GeneticAssociation Database, OMIM) have been associated with psoriasis insample sets of varying sizes and of different ethnicities; however,without more data from additional independent studies, it is difficultto draw statistically sound conclusions about whether these markers aretruly associated with disease. Thus, there remains a need for thediscovery of reliable markers that can associate themselves withpsoriasis, and in turn, would facilitate the diagnosis and treatment ofthe disease. The discovery of genetic markers which are useful inidentifying psoriasis individuals who are at increased risk fordeveloping psoriasis may lead to, for example, better therapeuticstrategies, economic models, and health care policy decisions.

SNPs

The genomes of all organisms undergo spontaneous mutation in the courseof their continuing evolution, generating variant forms of progenitorgenetic sequences (Gusella, Ann. Rev. Biochem. 55, 831-854 (1986)). Avariant form may confer an evolutionary advantage or disadvantagerelative to a progenitor form or may be neutral. In some instances, avariant form confers an evolutionary advantage to the species and iseventually incorporated into the DNA of many or most members of thespecies and effectively becomes the progenitor form. Additionally, theeffects of a variant form may be both beneficial and detrimental,depending on the circumstances. For example, a heterozygous sickle cellmutation confers resistance to malaria, but a homozygous sickle cellmutation is usually lethal. In many cases, both progenitor and variantforms survive and co-exist in a species population. The coexistence ofmultiple forms of a genetic sequence gives rise to geneticpolymorphisms, including SNPs.

Approximately 90% of all polymorphisms in the human genome are SNPs.SNPs are single base positions in DNA at which different alleles, oralternative nucleotides, exist in a population.

The SNP position (interchangeably referred to herein as SNP, SNP site,SNP locus, SNP marker, or marker) is usually preceded by and followed byhighly conserved sequences of the allele (e.g., sequences that vary inless than 1/100 or 1/1000 members of the populations). An individual maybe homozygous or heterozygous for an allele at each SNP position. A SNPcan, in some instances, be referred to as a “cSNP” to denote that thenucleotide sequence containing the SNP is an amino acid coding sequence.

A SNP may arise from a substitution of one nucleotide for another at thepolymorphic site. Substitutions can be transitions or transversions. Atransition is the replacement of one purine nucleotide by another purinenucleotide, or one pyrimidine by another pyrimidine. A transversion isthe replacement of a purine by a pyrimidine, or vice versa. A SNP mayalso be a single base insertion or deletion variant referred to as an“indel” (Weber et al., “Human diallelic insertion/deletionpolymorphisms”, Am J Hum Genet 2002 October; 71(4):854-62).

A synonymous codon change, or silent mutation/SNP (terms such as “SNP”,“polymorphism”, “mutation”, “mutant”, “variation”, and “variant” areused herein interchangeably), is one that does not result in a change ofamino acid due to the degeneracy of the genetic code. A substitutionthat changes a codon coding for one amino acid to a codon coding for adifferent amino acid (i.e., a non-synonymous codon change) is referredto as a missense mutation. A nonsense mutation results in a type ofnon-synonymous codon change in which a stop codon is formed, therebyleading to premature termination of a polypeptide chain and a truncatedprotein. A read-through mutation is another type of non-synonymous codonchange that causes the destruction of a stop codon, thereby resulting inan extended polypeptide product. While SNPs can be bi-, tri-, ortetra-allelic, the vast majority of the SNPs are bi-allelic, and arethus often referred to as “bi-allelic markers”, or “di-allelic markers”.

As used herein, references to SNPs and SNP genotypes include individualSNPs and/or haplotypes, which are groups of SNPs that are generallyinherited together. Haplotypes can have stronger correlations withdiseases or other phenotypic effects compared with individual SNPs, andtherefore may provide increased diagnostic accuracy in some cases(Stephens et al. Science 293, 489-493, 20 Jul. 2001). As used herein,the term “haplotype” refers to a set of two or more alleles on a singlechromosome. The term “diplotype” refers to a combination of twohaplotypes that a diploid individual carries. The term “doublediplotype”, also called “two-locus diplotype”, refers to a combinationof diplotypes at two distinct loci for an individual.

Causative SNPs are those SNPs that produce alterations in geneexpression or in the expression, structure, and/or function of a geneproduct, and therefore are most predictive of a possible clinicalphenotype. One such class includes SNPs falling within regions of genesencoding a polypeptide product, i.e. cSNPs. These SNPs may result in analteration of the amino acid sequence of the polypeptide product (i.e.,non-synonymous codon changes) and give rise to the expression of adefective or other variant protein. Furthermore, in the case of nonsensemutations, a SNP may lead to premature termination of a polypeptideproduct. Such variant products can result in a pathological condition,e.g., genetic disease. Examples of genes in which a SNP within a codingsequence causes a genetic disease include sickle cell anemia and cysticfibrosis.

Causative SNPs do not necessarily have to occur in coding regions;causative SNPs can occur in, for example, any genetic region that canultimately affect the expression, structure, and/or activity of theprotein encoded by a nucleic acid. Such genetic regions include, forexample, those involved in transcription, such as SNPs in transcriptionfactor binding domains, SNPs in promoter regions, in areas involved intranscript processing, such as SNPs at intron-exon boundaries that maycause defective splicing, or SNPs in mRNA processing signal sequencessuch as polyadenylation signal regions. Some SNPs that are not causativeSNPs nevertheless are in close association with, and therefore segregatewith, a disease-causing sequence In this situation, the presence of aSNP correlates with the presence of, or predisposition to, or anincreased risk in developing the disease. These SNPs, although notcausative, are nonetheless also useful for diagnostics, diseasepredisposition screening, and other uses.

An association study of a SNP and a specific disorder involvesdetermining the presence or frequency of the SNP allele in biologicalsamples from individuals with the disorder of interest, such aspsoriasis and related pathologies and comparing the information to thatof controls (i.e., individuals who do not have the disorder; controlsmay be also referred to as “healthy” or “normal” individuals) who arepreferably of similar age and race. The appropriate selection ofpatients and controls is important to the success of SNP associationstudies. Therefore, a pool of individuals with well-characterizedphenotypes is extremely desirable.

A SNP may be screened in diseased tissue samples or any biologicalsample obtained from a diseased individual, and compared to controlsamples, and selected for its increased (or decreased) occurrence in aspecific pathological condition, such as pathologies related topsoriasis, increased or decreased risk of developing psoriasis. Once astatistically significant association is established between one or moreSNP(s) and a pathological condition (or other phenotype) of interest,then the region around the SNP can optionally be thoroughly screened toidentify the causative genetic locus/sequence(s) (e.g., causativeSNP/mutation, gene, regulatory region, etc.) that influences thepathological condition or phenotype. Association studies may beconducted within the general population and are not limited to studiesperformed on related individuals in affected families (linkage studies).

Clinical trials have shown that patient response to treatment withpharmaceuticals is often heterogeneous. There is a continuing need toimprove pharmaceutical agent design and therapy. In that regard, SNPscan be used to identify patients most suited to therapy with particularpharmaceutical agents (this is often termed “pharmacogenomics”).Similarly, SNPs can be used to exclude patients from certain treatmentdue to the patient's increased likelihood of developing toxic sideeffects or their likelihood of not responding to the treatment.Pharmacogenomics can also be used in pharmaceutical research to assistthe drug development and selection process. (Linder et al. (1997),Clinical Chemistry, 43, 254; Marshall (1997), Nature Biotechnology, 15,1249; International Patent Application WO 97/40462, Spectra Biomedical;and Schafer et al. (1998), Nature Biotechnology, 16: 3).

SUMMARY OF THE INVENTION

The present invention relates to the identification of novel SNPs,unique combinations of such SNPs, haplotypes or diplotypes of SNPs thatare associated with psoriasis and in particular the increased ordecreased risk of developing psoriasis. The polymorphisms disclosedherein are directly useful as targets for the design of diagnosticreagents and the development of therapeutic agents for use in thediagnosis and treatment of psoriasis and related pathologies.

Based on the identification of SNPs associated with psoriasis, thepresent invention also provides methods of detecting these variants aswell as the design and preparation of detection reagents needed toaccomplish this task. The invention specifically provides, for example,novel SNPs in genetic sequences involved in psoriasis and relatedpathologies, isolated nucleic acid molecules (including, for example,DNA and RNA molecules) containing these SNPs, variant proteins encodedby nucleic acid molecules containing such SNPs, antibodies to theencoded variant proteins, computer-based and data storage systemscontaining the novel SNP information, methods of detecting these SNPs ina test sample, methods of identifying individuals who have an altered(i.e., increased or decreased) risk of developing psoriasis based on thepresence or absence of one or more particular nucleotides (alleles) atone or more SNP sites disclosed herein or the detection of one or moreencoded variant products (e.g., variant mRNA transcripts or variantproteins), methods of identifying individuals who are more or lesslikely to respond to a treatment (or more or less likely to experienceundesirable side effects from a treatment, etc.), methods of screeningfor compounds useful in the treatment of a disorder associated with avariant gene/protein, compounds identified by these methods, methods oftreating disorders mediated by a variant gene/protein, methods of usingthe novel SNPs of the present invention for human identification, etc.

In Tables 1-2, the present invention provides gene information,transcript sequences (SEQ ID NOS: 1-3), encoded amino acid sequences(SEQ ID NOS:4-6), genomic sequences (SEQ ID NOS:13-23), transcript-basedcontext sequences (SEQ ID NOS:7-12) and genomic-based context sequences(SEQ ID NOS:24-89) that contain the SNPs of the present invention, andextensive SNP information that includes observed alleles, allelefrequencies, populations/ethnic groups in which alleles have beenobserved, information about the type of SNP and corresponding functionaleffect, and, for cSNPs, information about the encoded polypeptideproduct. The transcript sequences (SEQ ID NOS:1-3), amino acid sequences(SEQ ID NOS:4-6), genomic sequences (SEQ ID NOS:13-23), transcript-basedSNP context sequences (SEQ ID NOS: 7-12), and genomic-based SNP contextsequences (SEQ ID NOS:24-89) are also provided in the Sequence Listing.

In a specific embodiment of the present invention, SNPs that occurnaturally in the human genome are provided as isolated nucleic acidmolecules. These SNPs are associated with psoriasis and relatedpathologies. In particular the SNPs are associated with either anincreased or decreased risk of developing psoriasis. As such, they canhave a variety of uses in the diagnosis and/or treatment of psoriasisand related pathologies. One aspect of the present invention relates toan isolated nucleic acid molecule comprising a nucleotide sequence inwhich at least one nucleotide is a SNP that is propriatory to Applera,or Celera. In an alternative embodiment, a nucleic acid of the inventionis an amplified polynucleotide, which is produced by amplification of aSNP-containing nucleic acid template. In another embodiment, theinvention provides for a variant protein that is encoded by a nucleicacid molecule containing a SNP disclosed herein.

In yet another embodiment of the invention, a reagent for detecting aSNP in the context of its naturally-occurring flanking nucleotidesequences (which can be, e.g., either DNA or mRNA) is provided. Inparticular, such a reagent may be in the form of, for example, ahybridization probe or an amplification primer that is useful in thespecific detection of a SNP of interest. In an alternative embodiment, aprotein detection reagent is used to detect a variant protein that isencoded by a nucleic acid molecule containing a SNP disclosed herein. Apreferred embodiment of a protein detection reagent is an antibody or anantigen-reactive antibody fragment.

Various embodiments of the invention also provide kits comprising SNPdetection reagents, and methods for detecting the SNPs disclosed hereinby employing detection reagents. In a specific embodiment, the presentinvention provides for a method of identifying an individual having anincreased or decreased risk of developing psoriasis by detecting thepresence or absence of one or more SNP alleles disclosed herein.Preferably, the SNP allele can be an allele of a SNP selected from thegroup consisting of rs321227, rs3212220, rs7709212, and rs6887695, or acombination of any number of them.

In another embodiment, a method for diagnosis of psoriasis and relatedpathologies by detecting the presence or absence of one or more SNPalleles disclosed herein is provided. In another embodiment, theinvention provides for a method of identifying an individual having analtered (either increased, or, decreased) risk of developing psoriasisby detecting the presence or absence of one or more SNP haplotypes,diplotypes, or two-locus diplotypes disclosed herein.

The SNP haplotype herein can be, for example, a combination ofrs3212227(A) and rs6887695(G), as a risk haplotype. The SNP haplotypeherein can also be, for example, a combination of rs3212227(C) andrs6887695(C), as a protective haplotype. The SNP haplotype herein canalso be, for example, a combination of rs11209026(G) and rs 7530511(C),which is a risk haplotype.

The SNP diplotype is preferably a combination of two copies of the riskhaplotype, rs3212227(A)/rs6887695(G). The SNP diplotype is alsopreferably a combination of two copies of the protective haplotype,rs3212227(C)/rs6887695(C). Similarly, the SNP diplotype can be acombination of two copies of the risk haplotype rs11209026(G) and rs7530511(C).

Furthermore, an exemplary embodiment of the present invention providesfor a method of identifying an individual having an increased risk ofdeveloping psoriasis by detecting the presence of absence of one or moretwo-locus diplotypes. Preferably, the two-locus diplotype herein can bea combination of rs3212227(A)/rs6887695(G)/rs11209026(G)/rs 7530511(C).

The nucleic acid molecules of the invention can be inserted in anexpression vector, such as to produce a variant protein in a host cell.Thus, the present invention also provides for a vector comprising aSNP-containing nucleic acid molecule, genetically-engineered host cellscontaining the vector, and methods for expressing a recombinant variantprotein using such host cells. In another specific embodiment, the hostcells, SNP-containing nucleic acid molecules, and/or variant proteinscan be used as targets in a method for screening and identifyingtherapeutic agents or pharmaceutical compounds useful in the treatmentof psoriasis and related pathologies.

An aspect of this invention is a method for treating psoriasis in ahuman subject wherein said human subject harbors a SNP, gene,transcript, and/or encoded protein identified in Tables 1-2, whichmethod comprises administering to said human subject a therapeuticallyor prophylactically effective amount of one or more agents counteractingthe effects of the disease, such as by inhibiting (or stimulating) theactivity of the gene, transcript, and/or encoded protein identified inTables 1-2.

Another aspect of this invention is a method for identifying an agentuseful in therapeutically or prophylactically treating psoriasis andrelated pathologies in a human subject wherein said human subjectharbors a SNP, gene, transcript, and/or encoded protein identified inTables 1-2, which method comprises contacting the gene, transcript, orencoded protein with a candidate agent under conditions suitable toallow formation of a binding complex between the gene, transcript, orencoded protein and the candidate agent and detecting the formation ofthe binding complex, wherein the presence of the complex identifies saidagent.

Another aspect of this invention is a method for treating psoriasis andrelated pathologies in a human subject, which method comprises:

(i) determining that said human subject harbors a SNP, gene, transcript,and/or encoded protein identified in Tables 1-2, and

(ii) administering to said subject a therapeutically or prophylacticallyeffective amount of one or more agents counteracting the effects of thedisease.

Yet another aspect of this invention is a method for evaluating thesuitability of a patient for psoriasis treatment comprising determiningthe genotype of said patient with respect to a particular set of SNPmarkers, said SNP markers comprising a plurality of individual SNPsranging from two to seven SNPs in Table 1 or Table 2, and calculating ascore using an appropriate algorithm based on the genotype of saidpatient, the resulting score being indicative of the suitability of saidpatient undergoing psoriasis treatment.

Another aspect of the invention is a method of treating a psoriasispatient comprising administering an appropriate drug in atherapeutically effective amount to said psoriasis patient whosegenotype has been shown to contain a plurality of SNPs as described inTable 1 or Table 2.

Many other uses and advantages of the present invention will be apparentto those skilled in the art upon review of the detailed description ofthe preferred embodiments herein. Solely for clarity of discussion, theinvention is described in the sections below by way of non-limitingexamples.

Description of the Files Contained on the CD-R Named CD000011ORD-CDR

The CD-R named CD000011ORD-CDR contains the following five text (ASCII)files:

1) File SEQLIST_CD000011PRO.txt provides the Sequence Listing. TheSequence Listing provides the transcript sequences (SEQ ID NOS:1-3) andprotein sequences (SEQ ID NOS:4-6) as shown in Table 1, and genomicsequences (SEQ ID NOS:13-23) as shown in Table 2, for eachpsoriasis-associated gene that contains one or more SNPs of the presentinvention. Also provided in the Sequence Listing are context sequencesflanking each SNP, including both transcript-based context sequences asshown in Table 1 (SEQ ID NOS:7-12) and genomic-based context sequencesas shown in Table 2 (SEQ ID NOS:24-89). The context sequences generallyprovide 100 bp upstream (5′) and 100 bp downstream (3′) of each SNP,with the SNP in the middle of the context sequence, for a total of 200bp of context sequence surrounding each SNP. FileSEQLIST_CD000011ORD.txt is 1,240 KB in size, and was created on Aug. 28,2007.

The material contained on the CD-R labeled CRF is hereby incorporated byreference pursuant to 37 CFR 1.77(b)(4).

Description of Table 1 and Table 2

Table 1 and Table 2 (both provided on the CD-R) disclose the SNP andassociated gene/transcript/protein information of the present invention.For each gene, Table 1 and Table 2 each provide a header containinggene/transcript/protein information, followed by a transcript andprotein sequence (in Table 1) or genomic sequence (in Table 2), and thenSNP information regarding each SNP found in that gene/transcript.

NOTE: SNPs may be included in both Table 1 and Table 2; Table 1 presentsthe SNPs relative to their transcript sequences and encoded proteinsequences, whereas Table 2 presents the SNPs relative to their genomicsequences (in some instances Table 2 may also include, after the lastgene sequence, genomic sequences of one or more intergenic regions, aswell as SNP context sequences and other SNP information for any SNPsthat lie within these intergenic regions). SNPs can readily becross-referenced between Tables based on their hCV (or, in someinstances, hDV) identification numbers.

The gene/transcript/protein information includes:

-   -   a gene number (1 through n, where n=the total number of genes in        the Table)    -   a Celera hCG and UID internal identification numbers for the        gene    -   a Celera hCT and UID internal identification numbers for the        transcript (Table 1 only)    -   a public Genbank accession number (e.g., RefSeq NM number) for        the transcript (Table 1 only)    -   a Celera hCP and UID internal identification numbers for the        protein encoded by the hCT transcript (Table 1 only)    -   a public Genbank accession number (e.g., RefSeq NP number) for        the protein (Table 1 only)    -   an art-known gene symbol    -   an art-known gene/protein name    -   Celera genomic axis position (indicating start nucleotide        position-stop nucleotide position)    -   the chromosome number of the chromosome on which the gene is        located    -   an OMIM (Online Mendelian Inheritance in Man; Johns Hopkins        University/NCBI) public reference number for obtaining further        information regarding the medical significance of each gene    -   alternative gene/protein name(s) and/or symbol(s) in the OMIM        entry

NOTE: Due to the presence of alternative splice forms, multipletranscript/protein entries can be provided for a single gene entry inTable 1; i.e., for a single Gene Number, multiple entries may beprovided in series that differ in their transcript/protein informationand sequences.

Following the gene/transcript/protein information is a transcriptsequence and protein sequence (in Table 1), or a genomic sequence (inTable 2), for each gene, as follows:

-   -   transcript sequence (Table 1 only) (corresponding to SEQ ID        NOS:1-3 of the Sequence Listing), with SNPs identified by their        IUB codes (transcript sequences can include 5′ UTR, protein        coding, and 3′ UTR regions). (NOTE: If there are differences        between the nucleotide sequence of the hCT transcript and the        corresponding public transcript sequence identified by the        Genbank accession number, the hCT transcript sequence (and        encoded protein) is provided, unless the public sequence is a        RefSeq transcript sequence identified by an NM number, in which        case the RefSeq NM transcript sequence (and encoded protein) is        provided. However, whether the hCT transcript or RefSeq NM        transcript is used as the transcript sequence, the disclosed        SNPs are represented by their IUB codes within the transcript.)    -   the encoded protein sequence (Table 1 only) (corresponding to        SEQ ID NOS:4-6 of the Sequence Listing)    -   the genomic sequence of the gene (Table 2 only), including 6 kb        on each side of the gene boundaries (i.e., 6 kb on the 5′ side        of the gene plus 6 kb on the 3′ side of the gene) (corresponding        to SEQ ID NOS:13-23 of the Sequence Listing).

After the last gene sequence, Table 2 may include additional genomicsequences of intergenic regions (in such instances, these sequences areidentified as “Intergenic region:” followed by a numericalidentification number), as well as SNP context sequences and other SNPinformation for any SNPs that lie within each intergenic region (andsuch SNPs are identified as “INTERGENIC” for SNP type).

NOTE: The transcript, protein, and transcript-based SNP contextsequences are provided in both Table 1 and in the Sequence Listing. Thegenomic and genomic-based SNP context sequences are provided in bothTable 2 and in the Sequence Listing. SEQ ID NOS are indicated in Table 1for each transcript sequence (SEQ ID NOS:1-3), protein sequence (SEQ IDNOS:4-6), and transcript-based SNP context sequence (SEQ ID NOS:7-12),and SEQ ID NOS are indicated in Table 2 for each genomic sequence (SEQID NOS:13-23), and genomic-based SNP context sequence (SEQ IDNOS:24-89).

The SNP information includes:

-   -   context sequence (taken from the transcript sequence in Table 1,        and taken from the genomic sequence in Table 2) with the SNP        represented by its IUB code, including 100 bp upstream (5′) of        the SNP position plus 100 bp downstream (3′) of the SNP position        (the transcript-based SNP context sequences in Table 1 are        provided in the Sequence Listing as SEQ ID NOS:7-12; the        genomic-based SNP context sequences in Table 2 are provided in        the Sequence Listing as SEQ ID NOS:24-89).    -   Celera hCV internal identification number for the SNP (in some        instances, an “hDV” number is given instead of an “hCV” number)    -   SNP position [position of the SNP within the given transcript        sequence (Table 1) or within the given genomic sequence (Table        2)]    -   SNP source (may include any combination of one or more of the        following five codes, depending on which internal sequencing        projects and/or public databases the SNP has been observed in:        “Applera”=SNP observed during the re-sequencing of genes and        regulatory regions of 39 individuals, “Celera”=SNP observed        during shotgun sequencing and assembly of the Celera human        genome sequence, “Celera Diagnostics”=SNP observed during        re-sequencing of nucleic acid samples from individuals who have        a disease, “dbSNP”=SNP observed in the dbSNP public database,        “HGBASE”=SNP observed in the HGBASE public database, “HGMD”=SNP        observed in the Human Gene Mutation Database (HGMD) public        database, “HapMap”=SNP observed in the International HapMap        Project public database, “CSNP”=SNP observed in an internal        Applied Biosystems (Foster City, Calif.) database of coding SNPS        (cSNPs)) (NOTE: multiple “Applera” source entries for a single        SNP indicate that the same SNP was covered by multiple        overlapping amplification products and the re-sequencing results        (e.g., observed allele counts) from each of these amplification        products is being provided)    -   Population/allele/allele count information in the format of        [population1(first_allele,count|second_allele,count)population2(first_allele,count|second_allele,count)        total (first_allele,total count|second_allele,total count)]. The        information in this field includes populations/ethnic groups in        which particular SNP alleles have been observed        (“cau”=Caucasian, “his”=Hispanic, “chn”=Chinese, and        “afr”=African-American, “jpn”=Japanese, “ind”=Indian,        “mex”=Mexican, “ain”=“American Indian, “cra”=Celera donor,        “no_pop”=no population information available), identified SNP        alleles, and observed allele counts (within each population        group and total allele counts), where available [“−” in the        allele field represents a deletion allele of an        insertion/deletion (“indel”) polymorphism (in which case the        corresponding insertion allele, which may be comprised of one or        more nucleotides, is indicated in the allele field on the        opposite side of the “|”); “-” in the count field indicates that        allele count information is not available]. For certain SNPs        from the public dbSNP database, population/ethnic information is        indicated as follows (this population information is publicly        available in dbSNP): “HISP1”=human individual DNA (anonymized        samples) from 23 individuals of self-described HISPANIC        heritage; “PAC1”=human individual DNA (anonymized samples) from        24 individuals of self-described PACIFIC RIM heritage;        “CAUC1”=human individual DNA (anonymized samples) from 31        individuals of self-described CAUCASIAN heritage; “AFR1”=human        individual DNA (anonymized samples) from 24 individuals of        self-described AFRICAN/AFRICAN AMERICAN heritage; “P1”=human        individual DNA (anonymized samples) from 102 individuals of        self-described heritage; “PA130299515”; “SC_(—)12_A”=SANGER 12        DNAs of Asian origin from Corielle cell repositories, 6 of which        are male and 6 female; “SC_(—)12_C”=SANGER 12 DNAs of Caucasian        origin from Corielle cell repositories from the CEPH/UTAH        library. Six male and 6 female; “SC_(—)12_AA”=SANGER 12 DNAs of        African-American origin from Corielle cell repositories 6 of        which are male and 6 female; “SC_(—)95_C”=SANGER 95 DNAs of        Caucasian origin from Corielle cell repositories from the        CEPH/UTAH library; and “SC_(—)12_CA”=Caucasians-12 DNAs from        Corielle cell repositories that are from the CEPH/UTAH library.        Six male and 6 female.

NOTE: For SNPs of “Applera” SNP source, genes/regulatory regions of 39individuals (20 Caucasians and 19 African Americans) were re-sequencedand, since each SNP position is represented by two chromosomes in eachindividual (with the exception of SNPs on X and Y chromosomes in males,for which each SNP position is represented by a single chromosome), upto 78 chromosomes were genotyped for each SNP position. Thus, the sum ofthe African-American (“afr”) allele counts is up to 38, the sum of theCaucasian allele counts (“cau”) is up to 40, and the total sum of allallele counts is up to 78.

(NOTE: semicolons separate population/allele/count informationcorresponding to each indicated SNP source; i.e., if four SNP sourcesare indicated, such as “Celera”, “dbSNP”, “HGBASE”, and “HGMD”, thenpopulation/allele/count information is provided in four groups which areseparated by semicolons and listed in the same order as the listing ofSNP sources, with each population/allele/count information groupcorresponding to the respective SNP source based on order; thus, in thisexample, the first population/allele/count information group wouldcorrespond to the first listed SNP source (Celera) and the thirdpopulation/allele/count information group separated by semicolons wouldcorrespond to the third listed SNP source (HGBASE); ifpopulation/allele/count information is not available for any particularSNP source, then a pair of semicolons is still inserted as aplace-holder in order to maintain correspondence between the list of SNPsources and the corresponding listing of population/allele/countinformation)

-   -   SNP type (e.g., location within gene/transcript and/or predicted        functional effect) [“MIS-SENSE MUTATION”=SNP causes a change in        the encoded amino acid (i.e., a non-synonymous coding SNP);        “SILENT MUTATION”=SNP does not cause a change in the encoded        amino acid (i.e., a synonymous coding SNP); “STOP CODON        MUTATION”=SNP is located in a stop codon; “NONSENSE        MUTATION”=SNP creates or destroys a stop codon; “UTR 5”=SNP is        located in a 5′ UTR of a transcript; “UTR 3”=SNP is located in a        3′ UTR of a transcript; “PUTATIVE UTR 5”=SNP is located in a        putative 5′ UTR; “PUTATIVE UTR 3”=SNP is located in a putative        3′ UTR; “DONOR SPLICE SITE”=SNP is located in a donor splice        site (5′ intron boundary); “ACCEPTOR SPLICE SITE”=SNP is located        in an acceptor splice site (3′ intron boundary); “CODING        REGION”=SNP is located in a protein-coding region of the        transcript; “EXON”=SNP is located in an exon; “INTRON”=SNP is        located in an intron; “hmCS”=SNP is located in a human-mouse        conserved segment; “TFBS”=SNP is located in a transcription        factor binding site; “UNKNOWN”=SNP type is not defined;        “INTERGENIC”=SNP is intergenic, i.e., outside of any gene        boundary]    -   Protein coding information (Table 1 only), where relevant, in        the format of [protein SEQ ID NO:#, amino acid position, (amino        acid-1, codon1) (amino acid-2, codon2)]. The information in this        field includes SEQ ID NO of the encoded protein sequence,        position of the amino acid residue within the protein identified        by the SEQ ID NO that is encoded by the codon containing the        SNP, amino acids (represented by one-letter amino acid codes)        that are encoded by the alternative SNP alleles (in the case of        stop codons, “X” is used for the one-letter amino acid code),        and alternative codons containing the alternative SNP        nucleotides which encode the amino acid residues (thus, for        example, for missense mutation-type SNPs, at least two different        amino acids and at least two different codons are generally        indicated; for silent mutation-type SNPs, one amino acid and at        least two different codons are generally indicated, etc.). In        instances where the SNP is located outside of a protein-coding        region (e.g., in a UTR region), “None” is indicated following        the protein SEQ ID NO.

Description of Table 3

Table 3 provides sequences (SEQ ID NOS:90-200) of primers that have beensynthesized and used in the laboratory to assay certain SNPs byallele-specific PCR during the course of association studies to verifythe association of these SNPs with psoriasis (see Examples section).

Table 3 provides the following:

-   -   the column labeled “Marker” provides an identification number        (e.g., a public “rs” number or internal “hCV” number) for each        SNP site.    -   the column labeled “Alleles” designates the two alternative        alleles (i.e., nucleotides) at the SNP site. These alleles are        targeted by the allele-specific primers (the allele-specific        primers are shown as Primer 1 and Primer 2). Note that alleles        may be presented in Table 3 based on a different orientation        (i.e., the reverse complement) relative to how the same alleles        are presented in Tables 1-2.    -   the column labeled “Primer 1 (Allele-Specific Primer)” provides        an allele-specific primer that is specific for an allele        designated in the “Alleles” column.    -   the column labeled “Primer 2 (Allele-Specific Primer)” provides        an allele-specific primer that is specific for the other allele        designated in the “Alleles” column.    -   the column labeled “Common Primer” provides a common primer that        is used in conjunction with each of the allele-specific primers        (i.e., Primer 1 and Primer 2) and which hybridizes at a site        away from the SNP position.

All primer sequences are given in the 5′ to 3′ direction.

Each of the nucleotides designated in the “Alleles” column matches or isthe reverse complement of (depending on the orientation of the primerrelative to the designated allele) the 3′ nucleotide of theallele-specific primer (i.e., either Primer 1 or Primer 2) that isspecific for that allele.

Description of Table 4

Table 4 provides a list of LD SNPs that are related to and derived fromcertain interrogated SNPs. The interrogated SNPs, which are shown incolumn 1 (which indicates the hCV identification numbers of eachinterrogated SNP) and column 2 (which indicates the public rsidentification numbers of each interrogated SNP) of Table 4, arestatistically significantly associated with psoriasis as shown in thetables. These LD SNPs are provided as an example of SNPs which can alsoserve as markers for disease association based on their being in LD withan interrogated SNP. The criteria and process of selecting such LD SNPs,including the calculation of the r² value and the r² threshold value,are described in Example Four, below.

In Table 4, the column labeled “Interrogated SNP” presents each markeras identified by its unique hCV identification number. The columnlabeled “Interrogated rs” presents the publicly known identifier rsnumber for the corresponding hCV number. The column labeled “LD SNP”presents the hCV numbers of the LD SNPs that are derived from theircorresponding interrogated SNPs. The column labeled “LD SNP rs” presentsthe publicly known rs number for the corresponding hCV number. Thecolumn labeled “Power” presents the level of power where the r²threshold is set. For example, when power is set at 0.51, the thresholdr² value calculated therefrom is the minimum r² that an LD SNP must havein reference to an interrogated SNP, in order for the LD SNP to beclassified as a marker capable of being associated with a diseasephenotype at greater than 51% probability. The column labeled “Thresholdr²” presents the minimum value of r² that an LD SNP must meet inreference to an interrogated SNP in order to qualify as an LD SNP. Thecolumn labeled “r²” presents the actual r² value of the LD SNP inreference to the interrogated SNP to which it is related.

Description of Tables 5-22

Table 5 provides IL12B and IL23R region SNPs that are significant in acombined analysis across all three sample sets (the 95% confidenceintervals for the odds ratios do not cross 1).

Table 6 provides minor allele frequencies and allele-based associationof IL12B-associated SNPs with psoriasis.

Table 7 provides minor allele frequencies and allele-based associationof IL23R-associated SNPs with psoriasis.

Table 8 provides demographic, clinical, and pooling information forcertain samples used in the analyses, including the discovery sample setand the replication sample set 1.

Table 9 provides allele frequencies and allele-based association of 32SNPs with psoriasis using pooled DNAs.

Table 10 provides results of case-control analysis for rs3212227.

Table 11 provides minor allele frequencies and allele-based associationof IL12B-associated SNPs with psoriasis.

Table 12 provides two marker haplotypes for the IL12B region.

Table 13 provides three marker IL12B haplotypes including the putativepromoter polymorphism, ss52085993.

Table 14 provides allele frequencies and allele-based association ofmarkers in IL12B-related genes.

Table 15 provides two marker haplotypes for the IL23R gene.

Table 16 provides results of diplotype analysis for the IL12B SNPsrs3212227 and rs6887695.

Table 17 provides results of diplotype analysis for the IL23R SNPsrs7530511 and rs 11209026.

Table 18 provides two-locus diplotypes for IL12B and IL23R.

Table 19 provides genotype frequencies for certain significantlyassociated SNPs across sample sets.

Table 20 provides effect sizes for haplotypes atrs7530511-rs11209026-rs10889674.

Table 21 provides IL23R region sliding window haplotype association3-SNP windows.

Table 22 provides therapeutic agents that target IL12 or IL23 which arein active clinical development programs.

Throughout Tables 5-7 and 9-21, “OR” refers to the odds ratio, “95% CI”refers to the 95% confidence interval for the odds ratio, andOR_(common) and P_(comb) refer to the odds ratio and p-value,respectively, from a combined analysis.

Odds ratios (OR) greater than one indicate that a given allele (orcombination of alleles such as a haplotype, diplotype, or two-locusdiplotype) is a risk allele, whereas odds ratios less than one indicatethat a given allele is a non-risk allele (which may also be referred toas a protective allele).

For a given risk allele, the other alternative allele at the SNPposition may be considered a non-risk (i.e., protective) allele. For agiven non-risk (i.e., protective) allele, the other alternative alleleat the SNP position may be considered a risk allele.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides SNPs associated with psoriasis andrelated pathologies, nucleic acid molecules containing SNPs, methods andreagents for the detection of the SNPs disclosed herein, uses of theseSNPs for the development of detection reagents, and assays or kits thatutilize such reagents. The psoriasis-associated SNPs disclosed hereinare useful for diagnosing, screening for, and evaluating predispositionto psoriasis, including an increased or decreased risk of developingpsoriasis, the rate of progression of psoriasis, and related pathologiesin humans. Furthermore, such SNPs and their encoded products are usefultargets for the development of therapeutic agents.

A large number of SNPs have been identified from re-sequencing DNA from39 individuals, and they are indicated as “Applera” SNP source in Tables1-2. Their allele frequencies observed in each of the Caucasian andAfrican-American ethnic groups are provided. Additional SNPs includedherein were previously identified during shotgun sequencing and assemblyof the human genome, and they are indicated as “Celera” SNP source inTables 1-2. Furthermore, the information provided in Table 1-2,particularly the allele frequency information obtained from 39individuals and the identification of the precise position of each SNPwithin each gene/transcript, allows haplotypes (i.e., groups of SNPsthat are co-inherited) to be readily inferred. The present inventionencompasses SNP haplotypes, as well as individual SNPs.

Thus, the present invention provides individual SNPs associated withpsoriasis, as well as combinations of SNPs and haplotypes in geneticregions associated with psoriasis, polymorphic/variant transcriptsequences (SEQ ID NOS:1-3) and genomic sequences (SEQ ID NOS:13-23)containing SNPs, encoded amino acid sequences (SEQ ID NOS: 4-6), andboth transcript-based SNP context sequences (SEQ ID NOS: 7-12) andgenomic-based SNP context sequences (SEQ ID NOS:24-89) (transcriptsequences, protein sequences, and transcript-based SNP context sequencesare provided in Table 1 and the Sequence Listing; genomic sequences andgenomic-based SNP context sequences are provided in Table 2 and theSequence Listing), methods of detecting these polymorphisms in a testsample, methods of determining the risk of an individual of having ordeveloping psoriasis, methods of screening for compounds useful fortreating disorders associated with a variant gene/protein such aspsoriasis, compounds identified by these screening methods, methods ofusing the disclosed SNPs to select a treatment strategy, methods oftreating a disorder associated with a variant gene/protein (i.e.,therapeutic methods), and methods of using the SNPs of the presentinvention for human identification.

The present invention provides novel SNPs associated with psoriasis andrelated pathologies, as well as SNPs that were previously known in theart, but were not previously known to be associated with psoriasis.Accordingly, the present invention provides novel compositions andmethods based on the novel SNPs disclosed herein, and also providesnovel methods of using the known, but previously unassociated, SNPs inmethods relating to psoriasis (e.g., for diagnosing psoriasis, etc.). InTables 1-2, known SNPs are identified based on the public database inwhich they have been observed, which is indicated as one or more of thefollowing SNP types: “dbSNP”=SNP observed in dbSNP, “HGBASE”=SNPobserved in HGBASE, and “HGMD”=SNP observed in the Human Gene MutationDatabase (HGMD). Novel SNPs for which the SNP source is only “Applera”and none other, i.e., those that have not been observed in any publicdatabases and which were also not observed during shotgun sequencing andassembly of the Celera human genome sequence (i.e., “Celera” SNPsource), are indicated in the tables.

Particular SNP alleles of the present invention can be associated witheither an increased risk of having or developing psoriasis and relatedpathologies, or a decreased risk of having or developing psoriasis. SNPalleles that are associated with a decreased risk of having ordeveloping psoriasis may be referred to as “protective” alleles, and SNPalleles that are associated with an increased risk of having ordeveloping psoriasis may be referred to as “susceptibility” alleles,“risk” alleles, or “risk factors”. Thus, whereas certain SNPs (or theirencoded products) can be assayed to determine whether an individualpossesses a SNP allele that is indicative of an increased risk of havingor developing psoriasis (i.e., a susceptibility allele), other SNPs (ortheir encoded products) can be assayed to determine whether anindividual possesses a SNP allele that is indicative of a decreased riskof having or developing psoriasis (i.e., a protective allele).Similarly, particular SNP alleles of the present invention can beassociated with either an increased or decreased likelihood ofresponding to a particular treatment or therapeutic compound, or anincreased or decreased likelihood of experiencing toxic effects from aparticular treatment or therapeutic compound. The term “altered” may beused herein to encompass either of these two possibilities (e.g., anincreased or a decreased risk/likelihood).

Those skilled in the art will readily recognize that nucleic acidmolecules may be double-stranded molecules and that reference to aparticular site on one strand refers, as well, to the corresponding siteon a complementary strand. In defining a SNP position, SNP allele, ornucleotide sequence, reference to an adenine, a thymine (uridine), acytosine, or a guanine at a particular site on one strand of a nucleicacid molecule also defines the thymine (uridine), adenine, guanine, orcytosine (respectively) at the corresponding site on a complementarystrand of the nucleic acid molecule. Thus, reference may be made toeither strand in order to refer to a particular SNP position, SNPallele, or nucleotide sequence. Probes and primers, may be designed tohybridize to either strand and SNP genotyping methods disclosed hereinmay generally target either strand. Throughout the specification, inidentifying a SNP position, reference is generally made to theprotein-encoding strand, only for the purpose of convenience.

References to variant peptides, polypeptides, or proteins of the presentinvention include peptides, polypeptides, proteins, or fragmentsthereof, that contain at least one amino acid residue that differs fromthe corresponding amino acid sequence of the art-knownpeptide/polypeptide/protein (the art-known protein may beinterchangeably referred to as the “wild-type”, “reference”, or “normal”protein). Such variant peptides/polypeptides/proteins can result from acodon change caused by a nonsynonymous nucleotide substitution at aprotein-coding SNP position (i.e., a missense mutation) disclosed by thepresent invention. Variant peptides/polypeptides/proteins of the presentinvention can also result from a nonsense mutation, i.e., a SNP thatcreates a premature stop codon, a SNP that generates a read-throughmutation by abolishing a stop codon, or due to any SNP disclosed by thepresent invention that otherwise alters the structure,function/activity, or expression of a protein, such as a SNP in aregulatory region (e.g. a promoter or enhancer) or a SNP that leads toalternative or defective splicing, such as a SNP in an intron or a SNPat an exon/intron boundary. As used herein, the terms “polypeptide”,“peptide”, and “protein” are used interchangeably.

IL12 and IL23 Therapeutics/Pharmacogenomics in Inflammatory andAutoimmune Disorders

Exemplary embodiments of the invention provide SNPs in IL12 and IL23that are associated with psoriasis (as shown in the tables). These SNPshave a variety of therapeutic and pharmacogenomic uses related to thetreatment of psoriasis, as well as other inflammatory and autoimmunedisorders such as Crohn's disease, ulcerative colitis, ankylosingspondylitis, rheumatoid arthritis, and multiple sclerosis. Thepsoriasis-associated SNPs provided herein may be used, for example, todetermine variability between different individuals in their response toan inflammatory or autoimmune disease therapy (e.g., a psoriasis therapyor a therapy for Crohn's disease, ulcerative colitis, ankylosingspondylitis, rheumatoid arthritis, multiple sclerosis, or otherinflammatory or autoimmune disorder) such as to predict whether anindividual will respond positively to a particular therapy, to determinethe most effective therapeutic agent (e.g., antibody, small moleculecompound, nucleic acid agent, etc.) to use to treat an individual, todetermine whether a particular therapeutic agent should or should not beadministered to an individual (e.g., by predicting whether theindividual is likely to positively respond to the therapy or bypredicting whether the individual will experience toxic or other otherundesirable side effects or is unlikely to respond to the therapy), orto determine the therapeutic regimen to use for an individual such asthe dosage or frequency of dosing of a therapeutic agent for aparticular individual. For example, Table 22 provides examples oftherapeutic agents in clinical development that target IL12 or IL23which the SNPs provided herein can be used in conjunction with. Thetherapeutic agents provided in Table 22 are examples of therapeuticagents that may be used to treat psoriasis or other inflammatory orautoimmune disorders, such as by modulating (e.g., inhibiting orstimulating) IL12 or IL23. Therapeutic agents that directly modulateIL12 or IL23 may be used to treat psoriasis or otherinflammatory/autoimmune disorders and, furthermore, therapeutic agentsthat target proteins that interact with IL12 or IL23 or are otherwise inIL12 or IL23 pathways may be used to indirectly modulate IL12 or IL23 tothereby treat psoriasis or other inflammatory/autoimmune disorders. Anytherapeutic agents such as these may be used in conjunction with theSNPs provided herein.

As one specific example, the IL12B and IL23R psoriasis-associated SNPsprovided herein may be used to predict whether an individual willrespond positively to anti-IL-12p40 antibody therapy and/or to determinethe most effective dosage of this therapy. This facilitates decisionmaking by medical practitioners, such as in deciding whether toadminister this therapy to a particular individual or select anothertherapy that may be better suited to the individual, or to use aparticular dosage, dosing schedule, or to modify other aspects of atherapeutic regimen to effectively treat the individual, for example.

In addition to medical treatment, these uses may also be applied, forexample, in the context of clinical trials of a therapeutic agent (e.g.,a therapeutic agent that targets IL12 or IL23 for the treatment ofpsoriasis, Crohn's disease, ulcerative colitis, ankylosing spondylitis,rheumatoid arthritis, multiple sclerosis, or other inflammatory orautoimmune disorders), such as to include particular individuals in aclinical trial who are predicted to positively respond to thetherapeutic agent based on the SNPs provided herein and/or to excludeparticular individuals from a clinical trial who are predicted to notpositively respond to the therapeutic agent based on the SNPs providedherein. By using the SNPs provided herein to target a therapeutic agentto individuals who are more likely to positively respond to the agent,the therapeutic agent is more likely to succeed in clinical trials byshowing positive efficacy and to therefore satisfy the FDA requirementsfor approval. Additionally, individuals who are more likely toexperience toxic or other undesirable side effects may be excluded frombeing administered the therapeutic agent. Furthermore, by using the SNPsprovided herein to determine an effective dosage or dosing frequency,for example, the therapeutic agent may be less likely to exhibittoxicity or other undesirable side effects, as well as more likely toachieve positive efficacy.

Isolated Nucleic Acid Molecules and SNP Detection Reagents & Kits

Tables 1 and 2 provide a variety of information about each SNP of thepresent invention that is associated with psoriasis, including thetranscript sequences (SEQ ID NOS:1-3), genomic sequences (SEQ IDNOS:13-23), and protein sequences (SEQ ID NOS:4-6) of the encoded geneproducts (with the SNPs indicated by IUB codes in the nucleic acidsequences). In addition, Tables 1 and 2 include SNP context sequences,which generally include 100 nucleotide upstream (5′) plus 100nucleotides downstream (3′) of each SNP position (SEQ ID NOS:7-12correspond to transcript-based SNP context sequences disclosed in Table1, and SEQ ID NOS:24-89 correspond to genomic-based context sequencesdisclosed in Table 2), the alternative nucleotides (alleles) at each SNPposition, and additional information about the variant where relevant,such as SNP type (coding, missense, splice site, UTR, etc.), humanpopulations in which the SNP was observed, observed allele frequencies,information about the encoded protein, etc.

Isolated Nucleic Acid Molecules

The present invention provides isolated nucleic acid molecules thatcontain one or more SNPs disclosed Table 1 and/or Table 2. Preferredisolated nucleic acid molecules contain one or more SNPs identified asApplera or Celera proprietary. Isolated nucleic acid moleculescontaining one or more SNPs disclosed in at least one of Tables 1-2 maybe interchangeably referred to throughout the present text as“SNP-containing nucleic acid molecules”. Isolated nucleic acid moleculesmay optionally encode a full-length variant protein or fragment thereof.The isolated nucleic acid molecules of the present invention alsoinclude probes and primers (which are described in greater detail belowin the section entitled “SNP Detection Reagents”), which may be used forassaying the disclosed SNPs, and isolated full-length genes,transcripts, cDNA molecules, and fragments thereof, which may be usedfor such purposes as expressing an encoded protein.

As used herein, an “isolated nucleic acid molecule” generally is onethat contains a SNP of the present invention or one that hybridizes tosuch molecule such as a nucleic acid with a complementary sequence, andis separated from most other nucleic acids present in the natural sourceof the nucleic acid molecule. Moreover, an “isolated” nucleic acidmolecule, such as a cDNA molecule containing a SNP of the presentinvention, can be substantially free of other cellular material, orculture medium when produced by recombinant techniques, or chemicalprecursors or other chemicals when chemically synthesized. A nucleicacid molecule can be fused to other coding or regulatory sequences andstill be considered “isolated”. Nucleic acid molecules present innon-human transgenic animals, which do not naturally occur in theanimal, are also considered “isolated”. For example, recombinant DNAmolecules contained in a vector are considered “isolated”. Furtherexamples of “isolated” DNA molecules include recombinant DNA moleculesmaintained in heterologous host cells, and purified (partially orsubstantially) DNA molecules in solution. Isolated RNA molecules includein vivo or in vitro RNA transcripts of the isolated SNP-containing DNAmolecules of the present invention. Isolated nucleic acid moleculesaccording to the present invention further include such moleculesproduced synthetically.

Generally, an isolated SNP-containing nucleic acid molecule comprisesone or more SNP positions disclosed by the present invention withflanking nucleotide sequences on either side of the SNP positions. Aflanking sequence can include nucleotide residues that are naturallyassociated with the SNP site and/or heterologous nucleotide sequences.Preferably the flanking sequence is up to about 500, 300, 100, 60, 50,30, 25, 20, 15, 10, 8, or 4 nucleotides (or any other length in-between)on either side of a SNP position, or as long as the full-length gene orentire protein-coding sequence (or any portion thereof such as an exon),especially if the SNP-containing nucleic acid molecule is to be used toproduce a protein or protein fragment.

For full-length genes and entire protein-coding sequences, a SNPflanking sequence can be, for example, up to about 5 KB, 4 KB, 3 KB, 2KB, 1 KB on either side of the SNP. Furthermore, in such instances, theisolated nucleic acid molecule comprises exonic sequences (includingprotein-coding and/or non-coding exonic sequences), but may also includeintronic sequences. Thus, any protein coding sequence may be eithercontiguous or separated by introns. The important point is that thenucleic acid is isolated from remote and unimportant flanking sequencesand is of appropriate length such that it can be subjected to thespecific manipulations or uses described herein such as recombinantprotein expression, preparation of probes and primers for assaying theSNP position, and other uses specific to the SNP-containing nucleic acidsequences.

An isolated SNP-containing nucleic acid molecule can comprise, forexample, a full-length gene or transcript, such as a gene isolated fromgenomic DNA (e.g., by cloning or PCR amplification), a cDNA molecule, oran mRNA transcript molecule. Polymorphic transcript sequences areprovided in Table 1 and in the Sequence Listing (SEQ ID NOS:1-3), andpolymorphic genomic sequences are provided in Table 2 and in theSequence Listing (SEQ ID NOS:13-23). Furthermore, fragments of suchfull-length genes and transcripts that contain one or more SNPsdisclosed herein are also encompassed by the present invention, and suchfragments may be used, for example, to express any part of a protein,such as a particular functional domain or an antigenic epitope.

Thus, the present invention also encompasses fragments of the nucleicacid sequences provided in Tables 1-2 (transcript sequences are providedin Table 1 as SEQ ID NOS:1-3, genomic sequences are provided in Table 2as SEQ ID NOS:13-23, transcript-based SNP context sequences are providedin Table 1 as SEQ ID NO:7-12, and genomic-based SNP context sequencesare provided in Table 2 as SEQ ID NO:24-89) and their complements. Afragment typically comprises a contiguous nucleotide sequence at leastabout 8 or more nucleotides, more preferably at least about 12 or morenucleotides, and even more preferably at least about 16 or morenucleotides. Further, a fragment could comprise at least about 18, 20,22, 25, 30, 40, 50, 60, 80, 100, 150, 200, 250 or 500 (or any othernumber in-between) nucleotides in length. The length of the fragmentwill be based on its intended use. For example, the fragment can encodeepitope-bearing regions of a variant peptide or regions of a variantpeptide that differ from the normal/wild-type protein, or can be usefulas a polynucleotide probe or primer. Such fragments can be isolatedusing the nucleotide sequences provided in Table 1 and/or Table 2 forthe synthesis of a polynucleotide probe. A labeled probe can then beused, for example, to screen a cDNA library, genomic DNA library, ormRNA to isolate nucleic acid corresponding to the coding region.Further, primers can be used in amplification reactions, such as forpurposes of assaying one or more SNPs sites or for cloning specificregions of a gene.

An isolated nucleic acid molecule of the present invention furtherencompasses a SNP-containing polynucleotide that is the product of anyone of a variety of nucleic acid amplification methods, which are usedto increase the copy numbers of a polynucleotide of interest in anucleic acid sample. Such amplification methods are well known in theart, and they include but are not limited to, polymerase chain reaction(PCR) (U.S. Pat. Nos. 4,683,195; and 4,683,202; PCR Technology:Principles and Applications for DNA Amplification, ed. H. A. Erlich,Freeman Press, NY, N.Y., 1992), ligase chain reaction (LCR) (Wu andWallace, Genomics 4:560, 1989; Landegren et al., Science 241:1077,1988), strand displacement amplification (SDA) (U.S. Pat. Nos.5,270,184; and 5,422,252), transcription-mediated amplification (TMA)(U.S. Pat. No. 5,399,491), linked linear amplification (LLA) (U.S. Pat.No. 6,027,923), and the like, and isothermal amplification methods suchas nucleic acid sequence based amplification (NASBA), and self-sustainedsequence replication (Guatelli et al., Proc. Natl. Acad. Sci. USA 87:1874, 1990). Based on such methodologies, a person skilled in the artcan readily design primers in any suitable regions 5′ and 3′ to a SNPdisclosed herein. Such primers may be used to amplify DNA of any lengthso long that it contains the SNP of interest in its sequence.

As used herein, an “amplified polynucleotide” of the invention is aSNP-containing nucleic acid molecule whose amount has been increased atleast two fold by any nucleic acid amplification method performed invitro as compared to its starting amount in a test sample. In otherpreferred embodiments, an amplified polynucleotide is the result of atleast ten fold, fifty fold, one hundred fold, one thousand fold, or eventen thousand fold increase as compared to its starting amount in a testsample. In a typical PCR amplification, a polynucleotide of interest isoften amplified at least fifty thousand fold in amount over theunamplified genomic DNA, but the precise amount of amplification neededfor an assay depends on the sensitivity of the subsequent detectionmethod used.

Generally, an amplified polynucleotide is at least about 16 nucleotidesin length. More typically, an amplified polynucleotide is at least about20 nucleotides in length. In a preferred embodiment of the invention, anamplified polynucleotide is at least about 30 nucleotides in length. Ina more preferred embodiment of the invention, an amplifiedpolynucleotide is at least about 32, 40, 45, 50, or 60 nucleotides inlength. In yet another preferred embodiment of the invention, anamplified polynucleotide is at least about 100, 200, 300, 400, or 500nucleotides in length. While the total length of an amplifiedpolynucleotide of the invention can be as long as an exon, an intron orthe entire gene where the SNP of interest resides, an amplified productis typically up to about 1,000 nucleotides in length (although certainamplification methods may generate amplified products greater than 1000nucleotides in length). More preferably, an amplified polynucleotide isnot greater than about 600-700 nucleotides in length. It is understoodthat irrespective of the length of an amplified polynucleotide, a SNP ofinterest may be located anywhere along its sequence.

In a specific embodiment of the invention, the amplified product is atleast about 201 nucleotides in length, comprises one of thetranscript-based context sequences or the genomic-based contextsequences shown in Tables 1-2. Such a product may have additionalsequences on its 5′ end or 3′ end or both. In another embodiment, theamplified product is about 101 nucleotides in length, and it contains aSNP disclosed herein. Preferably, the SNP is located at the middle ofthe amplified product (e.g., at position 101 in an amplified productthat is 201 nucleotides in length, or at position 51 in an amplifiedproduct that is 101 nucleotides in length), or within 1, 2, 3, 4, 5, 6,7, 8, 9, 10, 12, 15, or 20 nucleotides from the middle of the amplifiedproduct (however, as indicated above, the SNP of interest may be locatedanywhere along the length of the amplified product).

The present invention provides isolated nucleic acid molecules thatcomprise, consist of, or consist essentially of one or morepolynucleotide sequences that contain one or more SNPs disclosed herein,complements thereof, and SNP-containing fragments thereof.

Accordingly, the present invention provides nucleic acid molecules thatconsist of any of the nucleotide sequences shown in Table 1 and/or Table2 (transcript sequences are provided in Table 1 as SEQ ID NOS:1-3,genomic sequences are provided in Table 2 as SEQ ID NOS:13-23,transcript-based SNP context sequences are provided in Table 1 as SEQ IDNO:7-12, and genomic-based SNP context sequences are provided in Table 2as SEQ ID NO:24-89), or any nucleic acid molecule that encodes any ofthe variant proteins provided in Table 1 (SEQ ID NOS:4-6). A nucleicacid molecule consists of a nucleotide sequence when the nucleotidesequence is the complete nucleotide sequence of the nucleic acidmolecule.

The present invention further provides nucleic acid molecules thatconsist essentially of any of the nucleotide sequences shown in Table 1and/or Table 2 (transcript sequences are provided in Table 1 as SEQ IDNOS:1-3, genomic sequences are provided in Table 2 as SEQ ID NOS:13-23,transcript-based SNP context sequences are provided in Table 1 as SEQ IDNO:7-12, and genomic-based SNP context sequences are provided in Table 2as SEQ ID NO:24-89), or any nucleic acid molecule that encodes any ofthe variant proteins provided in Table 1 (SEQ ID NOS:4-6). A nucleicacid molecule consists essentially of a nucleotide sequence when such anucleotide sequence is present with only a few additional nucleotideresidues in the final nucleic acid molecule.

The present invention further provides nucleic acid molecules thatcomprise any of the nucleotide sequences shown in Table 1 and/or Table 2or a SNP-containing fragment thereof (transcript sequences are providedin Table 1 as SEQ ID NOS:1-3, genomic sequences are provided in Table 2as SEQ ID NOS:13-23, transcript-based SNP context sequences are providedin Table 1 as SEQ ID NO:7-12, and genomic-based SNP context sequencesare provided in Table 2 as SEQ ID NO:24-89), or any nucleic acidmolecule that encodes any of the variant proteins provided in Table 1(SEQ ID NOS:4-6). A nucleic acid molecule comprises a nucleotidesequence when the nucleotide sequence is at least part of the finalnucleotide sequence of the nucleic acid molecule. In such a fashion, thenucleic acid molecule can be only the nucleotide sequence or haveadditional nucleotide residues, such as residues that are naturallyassociated with it or heterologous nucleotide sequences. Such a nucleicacid molecule can have one to a few additional nucleotides or cancomprise many more additional nucleotides. A brief description of howvarious types of these nucleic acid molecules can be readily made andisolated is provided below, and such techniques are well known to thoseof ordinary skill in the art (Sambrook and Russell, 2000, MolecularCloning: A Laboratory Manual, Cold Spring Harbor Press, NY).

The isolated nucleic acid molecules can encode mature proteins plusadditional amino or carboxyl-terminal amino acids or both, or aminoacids interior to the mature peptide (when the mature form has more thanone peptide chain, for instance). Such sequences may play a role inprocessing of a protein from precursor to a mature form, facilitateprotein trafficking, prolong or shorten protein half-life, or facilitatemanipulation of a protein for assay or production. As generally is thecase in situ, the additional amino acids may be processed away from themature protein by cellular enzymes.

Thus, the isolated nucleic acid molecules include, but are not limitedto, nucleic acid molecules having a sequence encoding a peptide alone, asequence encoding a mature peptide and additional coding sequences suchas a leader or secretory sequence (e.g., a pre-pro or pro-proteinsequence), a sequence encoding a mature peptide with or withoutadditional coding sequences, plus additional non-coding sequences, forexample introns and non-coding 5′ and 3′ sequences such as transcribedbut untranslated sequences that play a role in, for example,transcription, mRNA processing (including splicing and polyadenylationsignals), ribosome binding, and/or stability of mRNA. In addition, thenucleic acid molecules may be fused to heterologous marker sequencesencoding, for example, a peptide that facilitates purification.

Isolated nucleic acid molecules can be in the form of RNA, such as mRNA,or in the form DNA, including cDNA and genomic DNA, which may beobtained, for example, by molecular cloning or produced by chemicalsynthetic techniques or by a combination thereof (Sambrook and Russell,2000, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Press,NY). Furthermore, isolated nucleic acid molecules, particularly SNPdetection reagents such as probes and primers, can also be partially orcompletely in the form of one or more types of nucleic acid analogs,such as peptide nucleic acid (PNA) (U.S. Pat. Nos. 5,539,082; 5,527,675;5,623,049; 5,714,331). The nucleic acid, especially DNA, can bedouble-stranded or single-stranded. Single-stranded nucleic acid can bethe coding strand (sense strand) or the complementary non-coding strand(anti-sense strand). DNA, RNA, or PNA segments can be assembled, forexample, from fragments of the human genome (in the case of DNA or RNA)or single nucleotides, short oligonucleotide linkers, or from a seriesof oligonucleotides, to provide a synthetic nucleic acid molecule.Nucleic acid molecules can be readily synthesized using the sequencesprovided herein as a reference; oligonucleotide and PNA oligomersynthesis techniques are well known in the art (see, e.g., Corey,“Peptide nucleic acids: expanding the scope of nucleic acidrecognition”, Trends Biotechnol. 1997 June; 15(6):224-9, and Hyrup etal., “Peptide nucleic acids (PNA): synthesis, properties and potentialapplications”, Bioorg Med Chem. 1996 January; 4(1):5-23). Furthermore,large-scale automated oligonucleotide/PNA synthesis (including synthesison an array or bead surface or other solid support) can readily beaccomplished using commercially available nucleic acid synthesizers,such as the Applied Biosystems (Foster City, Calif.) 3900High-Throughput DNA Synthesizer or Expedite 8909 Nucleic Acid SynthesisSystem, and the sequence information provided herein.

The present invention encompasses nucleic acid analogs that containmodified, synthetic, or non-naturally occurring nucleotides orstructural elements or other alternative/modified nucleic acidchemistries known in the art. Such nucleic acid analogs are useful, forexample, as detection reagents (e.g., primers/probes) for detecting oneor more SNPs identified in Table 1 and/or Table 2. Furthermore,kits/systems (such as beads, arrays, etc.) that include these analogsare also encompassed by the present invention. For example, PNAoligomers that are based on the polymorphic sequences of the presentinvention are specifically contemplated. PNA oligomers are analogs ofDNA in which the phosphate backbone is replaced with a peptide-likebackbone (Lagriffoul et al., Bioorganic & Medicinal Chemistry Letters,4: 1081-1082 (1994), Petersen et al., Bioorganic & Medicinal ChemistryLetters, 6: 793-796 (1996), Kumar et al., Organic Letters 3(9):1269-1272 (2001), WO96/04000). PNA hybridizes to complementary RNA orDNA with higher affinity and specificity than conventionaloligonucleotides and oligonucleotide analogs. The properties of PNAenable novel molecular biology and biochemistry applicationsunachievable with traditional oligonucleotides and peptides.

Additional examples of nucleic acid modifications that improve thebinding properties and/or stability of a nucleic acid include the use ofbase analogs such as inosine, intercalators (U.S. Pat. No. 4,835,263)and the minor groove binders (U.S. Pat. No. 5,801,115). Thus, referencesherein to nucleic acid molecules, SNP-containing nucleic acid molecules,SNP detection reagents (e.g., probes and primers),oligonucleotides/polynucleotides include PNA oligomers and other nucleicacid analogs. Other examples of nucleic acid analogs andalternative/modified nucleic acid chemistries known in the art aredescribed in Current Protocols in Nucleic Acid Chemistry, John Wiley &Sons, N.Y. (2002).

The present invention further provides nucleic acid molecules thatencode fragments of the variant polypeptides disclosed herein as well asnucleic acid molecules that encode obvious variants of such variantpolypeptides. Such nucleic acid molecules may be naturally occurring,such as paralogs (different locus) and orthologs (different organism),or may be constructed by recombinant DNA methods or by chemicalsynthesis. Non-naturally occurring variants may be made by mutagenesistechniques, including those applied to nucleic acid molecules, cells, ororganisms. Accordingly, the variants can contain nucleotidesubstitutions, deletions, inversions and insertions (in addition to theSNPs disclosed in Tables 1-2). Variation can occur in either or both thecoding and non-coding regions. The variations can produce conservativeand/or non-conservative amino acid substitutions.

Further variants of the nucleic acid molecules disclosed in Tables 1-2,such as naturally occurring allelic variants (as well as orthologs andparalogs) and synthetic variants produced by mutagenesis techniques, canbe identified and/or produced using methods well known in the art. Suchfurther variants can comprise a nucleotide sequence that shares at least70-80%, 80-85%, 85-90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%sequence identity with a nucleic acid sequence disclosed in Table 1and/or Table 2 (or a fragment thereof) and that includes a novel SNPallele disclosed in Table 1 and/or Table 2. Further, variants cancomprise a nucleotide sequence that encodes a polypeptide that shares atleast 70-80%, 80-85%, 85-90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or99% sequence identity with a polypeptide sequence disclosed in Table 1(or a fragment thereof) and that includes a novel SNP allele disclosedin Table 1 and/or Table 2. Thus, an aspect of the present invention thatis specifically contemplated are isolated nucleic acid molecules thathave a certain degree of sequence variation compared with the sequencesshown in Tables 1-2, but that contain a novel SNP allele disclosedherein. In other words, as long as an isolated nucleic acid moleculecontains a novel SNP allele disclosed herein, other portions of thenucleic acid molecule that flank the novel SNP allele can vary to somedegree from the specific transcript, genomic, and context sequencesshown in Tables 1-2, and can encode a polypeptide that varies to somedegree from the specific polypeptide sequences shown in Table 1.

To determine the percent identity of two amino acid sequences or twonucleotide sequences of two molecules that share sequence homology, thesequences are aligned for optimal comparison purposes (e.g., gaps can beintroduced in one or both of a first and a second amino acid or nucleicacid sequence for optimal alignment and non-homologous sequences can bedisregarded for comparison purposes). In a preferred embodiment, atleast 30%, 40%, 50%, 60%, 70%, 80%, or 90% or more of the length of areference sequence is aligned for comparison purposes. The amino acidresidues or nucleotides at corresponding amino acid positions ornucleotide positions are then compared. When a position in the firstsequence is occupied by the same amino acid residue or nucleotide as thecorresponding position in the second sequence, then the molecules areidentical at that position (as used herein, amino acid or nucleic acid“identity” is equivalent to amino acid or nucleic acid “homology”). Thepercent identity between the two sequences is a function of the numberof identical positions shared by the sequences, taking into account thenumber of gaps, and the length of each gap, which need to be introducedfor optimal alignment of the two sequences.

The comparison of sequences and determination of percent identitybetween two sequences can be accomplished using a mathematicalalgorithm. (Computational Molecular Biology, Lesk, A. M., ed., OxfordUniversity Press, New York, 1988; Biocomputing: Informatics and GenomeProjects, Smith, D. W., ed., Academic Press, New York, 1993; ComputerAnalysis of Sequence Data, Part 1, Griffin, A. M., and Griffin, H. G.,eds., Humana Press, New Jersey, 1994; Sequence Analysis in MolecularBiology, von Heinje, G., Academic Press, 1987; and Sequence AnalysisPrimer, Gribskov, M. and Devereux, J., eds., M Stockton Press, New York,1991). In a preferred embodiment, the percent identity between two aminoacid sequences is determined using the Needleman and Wunsch algorithm(J. Mol. Biol. (48):444-453 (1970)) which has been incorporated into theGAP program in the GCG software package, using either a Blossom 62matrix or a PAM250 matrix, and a gap weight of 16, 14, 12, 10, 8, 6, or4 and a length weight of 1, 2, 3, 4, 5, or 6.

In yet another preferred embodiment, the percent identity between twonucleotide sequences is determined using the GAP program in the GCGsoftware package (Devereux, J., et al., Nucleic Acids Res. 12(1):387(1984)), using a NWSgapdna.CMP matrix and a gap weight of 40, 50, 60,70, or 80 and a length weight of 1, 2, 3, 4, 5, or 6. In anotherembodiment, the percent identity between two amino acid or nucleotidesequences is determined using the algorithm of E. Myers and W. Miller(CABIOS, 4:11-17 (1989)) which has been incorporated into the ALIGNprogram (version 2.0), using a PAM120 weight residue table, a gap lengthpenalty of 12, and a gap penalty of 4.

The nucleotide and amino acid sequences of the present invention canfurther be used as a “query sequence” to perform a search againstsequence databases to, for example, identify other family members orrelated sequences. Such searches can be performed using the NBLAST andXBLAST programs (version 2.0) of Altschul, et al. (J. Mol. Biol.215:403-10 (1990)). BLAST nucleotide searches can be performed with theNBLAST program, score=100, wordlength=12 to obtain nucleotide sequenceshomologous to the nucleic acid molecules of the invention. BLAST proteinsearches can be performed with the XBLAST program, score=50,wordlength=3 to obtain amino acid sequences homologous to the proteinsof the invention. To obtain gapped alignments for comparison purposes,Gapped BLAST can be utilized as described in Altschul et al. (NucleicAcids Res. 25(17):3389-3402 (1997)). When utilizing BLAST and gappedBLAST programs, the default parameters of the respective programs (e.g.,XBLAST and NBLAST) can be used. In addition to BLAST, examples of othersearch and sequence comparison programs used in the art include, but arenot limited to, FASTA (Pearson, Methods Mol. Biol. 25, 365-389 (1994))and KERR (Dufresne et al., Nat Biotechnol 2002 December;20(12):1269-71). For further information regarding bioinformaticstechniques, see Current Protocols in Bioinformatics, John Wiley & Sons,Inc., N.Y.

The present invention further provides non-coding fragments of thenucleic acid molecules disclosed in Table 1 and/or Table 2. Preferrednon-coding fragments include, but are not limited to, promotersequences, enhancer sequences, intronic sequences, 5′ untranslatedregions (UTRs), 3′ untranslated regions, gene modulating sequences andgene termination sequences. Such fragments are useful, for example, incontrolling heterologous gene expression and in developing screens toidentify gene-modulating agents.

SNP Detection Reagents

In a specific aspect of the present invention, the SNPs disclosed inTable 1 and/or Table 2, and their associated transcript sequences(provided in Table 1 as SEQ ID NOS:1-3), genomic sequences (provided inTable 2 as SEQ ID NOS:13-23), and context sequences (transcript-basedcontext sequences are provided in Table 1 as SEQ ID NOS:7-12;genomic-based context sequences are provided in Table 2 as SEQ IDNOS:24-89), can be used for the design of SNP detection reagents. Asused herein, a “SNP detection reagent” is a reagent that specificallydetects a specific target SNP position disclosed herein, and that ispreferably specific for a particular nucleotide (allele) of the targetSNP position (i.e., the detection reagent preferably can differentiatebetween different alternative nucleotides at a target SNP position,thereby allowing the identity of the nucleotide present at the targetSNP position to be determined). Typically, such detection reagenthybridizes to a target SNP-containing nucleic acid molecule bycomplementary base-pairing in a sequence specific manner, anddiscriminates the target variant sequence from other nucleic acidsequences such as an art-known form in a test sample. An example of adetection reagent is a probe that hybridizes to a target nucleic acidcontaining one or more of the SNPs provided in Table 1 and/or Table 2.In a preferred embodiment, such a probe can differentiate betweennucleic acids having a particular nucleotide (allele) at a target SNPposition from other nucleic acids that have a different nucleotide atthe same target SNP position. In addition, a detection reagent mayhybridize to a specific region 5′ and/or 3′ to a SNP position,particularly a region corresponding to the context sequences provided inTable 1 and/or Table 2 (transcript-based context sequences are providedin Table 1 as SEQ ID NOS:7-12; genomic-based context sequences areprovided in Table 2 as SEQ ID NOS:24-89). Another example of a detectionreagent is a primer which acts as an initiation point of nucleotideextension along a complementary strand of a target polynucleotide. TheSNP sequence information provided herein is also useful for designingprimers, e.g. allele-specific primers, to amplify (e.g., using PCR) anySNP of the present invention.

In one preferred embodiment of the invention, a SNP detection reagent isan isolated or synthetic DNA or RNA polynucleotide probe or primer orPNA oligomer, or a combination of DNA, RNA and/or PNA, that hybridizesto a segment of a target nucleic acid molecule containing a SNPidentified in Table 1 and/or Table 2. A detection reagent in the form ofa polynucleotide may optionally contain modified base analogs,intercalators or minor groove binders. Multiple detection reagents suchas probes may be, for example, affixed to a solid support (e.g., arraysor beads) or supplied in solution (e.g., probe/primer sets for enzymaticreactions such as PCR, RT-PCR, TaqMan assays, or primer-extensionreactions) to form a SNP detection kit.

A probe or primer typically is a substantially purified oligonucleotideor PNA oligomer. Such oligonucleotide typically comprises a region ofcomplementary nucleotide sequence that hybridizes under stringentconditions to at least about 8, 10, 12, 16, 18, 20, 22, 25, 30, 40, 50,55, 60, 65, 70, 80, 90, 100, 120 (or any other number in-between) ormore consecutive nucleotides in a target nucleic acid molecule.Depending on the particular assay, the consecutive nucleotides caneither include the target SNP position, or be a specific region in closeenough proximity 5′ and/or 3′ to the SNP position to carry out thedesired assay.

Other preferred primer and probe sequences can readily be determinedusing the transcript sequences (SEQ ID NOS:1-3), genomic sequences (SEQID NOS:13-23), and SNP context sequences (transcript-based contextsequences are provided in Table 1 as SEQ ID NOS:7-12; genomic-basedcontext sequences are provided in Table 2 as SEQ ID NOS:24-89) disclosedin the Sequence Listing and in Tables 1-2. It will be apparent to one ofskill in the art that such primers and probes are directly useful asreagents for genotyping the SNPs of the present invention, and can beincorporated into any kit/system format.

In order to produce a probe or primer specific for a targetSNP-containing sequence, the gene/transcript and/or context sequencesurrounding the SNP of interest is typically examined using a computeralgorithm which starts at the 5′ or at the 3′ end of the nucleotidesequence. Typical algorithms will then identify oligomers of definedlength that are unique to the gene/SNP context sequence, have a GCcontent within a range suitable for hybridization, lack predictedsecondary structure that may interfere with hybridization, and/orpossess other desired characteristics or that lack other undesiredcharacteristics.

A primer or probe of the present invention is typically at least about 8nucleotides in length. In one embodiment of the invention, a primer or aprobe is at least about 10 nucleotides in length. In a preferredembodiment, a primer or a probe is at least about 12 nucleotides inlength. In a more preferred embodiment, a primer or probe is at leastabout 16, 17, 18, 19, 20, 21, 22, 23, 24 or 25 nucleotides in length.While the maximal length of a probe can be as long as the targetsequence to be detected, depending on the type of assay in which it isemployed, it is typically less than about 50, 60, 65, or 70 nucleotidesin length. In the case of a primer, it is typically less than about 30nucleotides in length. In a specific preferred embodiment of theinvention, a primer or a probe is within the length of about 18 andabout 28 nucleotides. However, in other embodiments, such as nucleicacid arrays and other embodiments in which probes are affixed to asubstrate, the probes can be longer, such as on the order of 30-70, 75,80, 90, 100, or more nucleotides in length (see the section belowentitled “SNP Detection Kits and Systems”).

For analyzing SNPs, it may be appropriate to use oligonucleotidesspecific for alternative SNP alleles. Such oligonucleotides which detectsingle nucleotide variations in target sequences may be referred to bysuch terms as “allele-specific oligonucleotides”, “allele-specificprobes”, or “allele-specific primers”. The design and use ofallele-specific probes for analyzing polymorphisms is described in,e.g., Mutation Detection A Practical Approach, ed. Cotton et al. OxfordUniversity Press, 1998; Saiki et al., Nature 324, 163-166 (1986);Dattagupta, EP235,726; and Saiki, WO 89/11548.

While the design of each allele-specific primer or probe depends onvariables such as the precise composition of the nucleotide sequencesflanking a SNP position in a target nucleic acid molecule, and thelength of the primer or probe, another factor in the use of primers andprobes is the stringency of the condition under which the hybridizationbetween the probe or primer and the target sequence is performed. Higherstringency conditions utilize buffers with lower ionic strength and/or ahigher reaction temperature, and tend to require a more perfect matchbetween probe/primer and a target sequence in order to form a stableduplex. If the stringency is too high, however, hybridization may notoccur at all. In contrast, lower stringency conditions utilize bufferswith higher ionic strength and/or a lower reaction temperature, andpermit the formation of stable duplexes with more mismatched basesbetween a probe/primer and a target sequence. By way of example and notlimitation, exemplary conditions for high stringency hybridizationconditions using an allele-specific probe are as follows:Prehybridization with a solution containing 5× standard saline phosphateEDTA (SSPE), 0.5% NaDodSO₄ (SDS) at 55° C., and incubating probe withtarget nucleic acid molecules in the same solution at the sametemperature, followed by washing with a solution containing 2×SSPE, and0.1% SDS at 55° C. or room temperature.

Moderate stringency hybridization conditions may be used forallele-specific primer extension reactions with a solution containing,e.g., about 50 mM KCl at about 46° C. Alternatively, the reaction may becarried out at an elevated temperature such as 60° C. In anotherembodiment, a moderately stringent hybridization condition suitable foroligonucleotide ligation assay (OLA) reactions wherein two probes areligated if they are completely complementary to the target sequence mayutilize a solution of about 100 mM KCl at a temperature of 46° C.

In a hybridization-based assay, allele-specific probes can be designedthat hybridize to a segment of target DNA from one individual but do nothybridize to the corresponding segment from another individual due tothe presence of different polymorphic forms (e.g., alternative SNPalleles/nucleotides) in the respective DNA segments from the twoindividuals. Hybridization conditions should be sufficiently stringentthat there is a significant detectable difference in hybridizationintensity between alleles, and preferably an essentially binaryresponse, whereby a probe hybridizes to only one of the alleles orsignificantly more strongly to one allele. While a probe may be designedto hybridize to a target sequence that contains a SNP site such that theSNP site aligns anywhere along the sequence of the probe, the probe ispreferably designed to hybridize to a segment of the target sequencesuch that the SNP site aligns with a central position of the probe(e.g., a position within the probe that is at least three nucleotidesfrom either end of the probe). This design of probe generally achievesgood discrimination in hybridization between different allelic forms.

In another embodiment, a probe or primer may be designed to hybridize toa segment of target DNA such that the SNP aligns with either the 5′ mostend or the 3′ most end of the probe or primer. In a specific preferredembodiment which is particularly suitable for use in a oligonucleotideligation assay (U.S. Pat. No. 4,988,617), the 3′ most nucleotide of theprobe aligns with the SNP position in the target sequence.

Oligonucleotide probes and primers may be prepared by methods well knownin the art. Chemical synthetic methods include, but are limited to, thephosphotriester method described by Narang et al., 1979, Methods inEnzymology 68:90; the phosphodiester method described by Brown et al.,1979, Methods in Enzymology 68:109, the diethylphosphoamidate methoddescribed by Beaucage et al., 1981, Tetrahedron Letters 22:1859; and thesolid support method described in U.S. Pat. No. 4,458,066.

Allele-specific probes are often used in pairs (or, less commonly, insets of 3 or 4, such as if a SNP position is known to have 3 or 4alleles, respectively, or to assay both strands of a nucleic acidmolecule for a target SNP allele), and such pairs may be identicalexcept for a one nucleotide mismatch that represents the allelicvariants at the SNP position. Commonly, one member of a pair perfectlymatches a reference form of a target sequence that has a more common SNPallele (i.e., the allele that is more frequent in the target population)and the other member of the pair perfectly matches a form of the targetsequence that has a less common SNP allele (i.e., the allele that israrer in the target population). In the case of an array, multiple pairsof probes can be immobilized on the same support for simultaneousanalysis of multiple different polymorphisms.

In one type of PCR-based assay, an allele-specific primer hybridizes toa region on a target nucleic acid molecule that overlaps a SNP positionand only primes amplification of an allelic form to which the primerexhibits perfect complementarity (Gibbs, 1989, Nucleic Acid Res. 172427-2448). Typically, the primer's 3′-most nucleotide is aligned withand complementary to the SNP position of the target nucleic acidmolecule. This primer is used in conjunction with a second primer thathybridizes at a distal site. Amplification proceeds from the twoprimers, producing a detectable product that indicates which allelicform is present in the test sample. A control is usually performed witha second pair of primers, one of which shows a single base mismatch atthe polymorphic site and the other of which exhibits perfectcomplementarity to a distal site. The single-base mismatch preventsamplification or substantially reduces amplification efficiency, so thateither no detectable product is formed or it is formed in lower amountsor at a slower pace. The method generally works most effectively whenthe mismatch is at the 3′-most position of the oligonucleotide (i.e.,the 3′-most position of the oligonucleotide aligns with the target SNPposition) because this position is most destabilizing to elongation fromthe primer (see, e.g., WO 93/22456). This PCR-based assay can beutilized as part of the TaqMan assay, described below.

In a specific embodiment of the invention, a primer of the inventioncontains a sequence substantially complementary to a segment of a targetSNP-containing nucleic acid molecule except that the primer has amismatched nucleotide in one of the three nucleotide positions at the3′-most end of the primer, such that the mismatched nucleotide does notbase pair with a particular allele at the SNP site. In a preferredembodiment, the mismatched nucleotide in the primer is the second fromthe last nucleotide at the 3′-most position of the primer. In a morepreferred embodiment, the mismatched nucleotide in the primer is thelast nucleotide at the 3′-most position of the primer.

In another embodiment of the invention, a SNP detection reagent of theinvention is labeled with a fluorogenic reporter dye that emits adetectable signal. While the preferred reporter dye is a fluorescentdye, any reporter dye that can be attached to a detection reagent suchas an oligonucleotide probe or primer is suitable for use in theinvention. Such dyes include, but are not limited to, Acridine, AMCA,BODIPY, Cascade Blue, Cy2, Cy3, Cy5, Cy7, Dabcyl, Edans, Eosin,Erythrosin, Fluorescein, 6-Fam, Tet, Joe, Hex, Oregon Green, Rhodamine,Rhodol Green, Tamra, Rox, and Texas Red.

In yet another embodiment of the invention, the detection reagent may befurther labeled with a quencher dye such as Tamra, especially when thereagent is used as a self-quenching probe such as a TaqMan (U.S. Pat.Nos. 5,210,015 and 5,538,848) or Molecular Beacon probe (U.S. Pat. Nos.5,118,801 and 5,312,728), or other stemless or linear beacon probe(Livak et al., 1995, PCR Method Appl. 4:357-362; Tyagi et al., 1996,Nature Biotechnology 14: 303-308; Nazarenko et al., 1997, Nucl. AcidsRes. 25:2516-2521; U.S. Pat. Nos. 5,866,336 and 6,117,635).

The detection reagents of the invention may also contain other labels,including but not limited to, biotin for streptavidin binding, haptenfor antibody binding, and oligonucleotide for binding to anothercomplementary oligonucleotide such as pairs of zipcodes.

The present invention also contemplates reagents that do not contain (orthat are complementary to) a SNP nucleotide identified herein but thatare used to assay one or more SNPs disclosed herein. For example,primers that flank, but do not hybridize directly to a target SNPposition provided herein are useful in primer extension reactions inwhich the primers hybridize to a region adjacent to the target SNPposition (i.e., within one or more nucleotides from the target SNPsite). During the primer extension reaction, a primer is typically notable to extend past a target SNP site if a particular nucleotide(allele) is present at that target SNP site, and the primer extensionproduct can be detected in order to determine which SNP allele ispresent at the target SNP site. For example, particular ddNTPs aretypically used in the primer extension reaction to terminate primerextension once a ddNTP is incorporated into the extension product (aprimer extension product which includes a ddNTP at the 3′-most end ofthe primer extension product, and in which the ddNTP is a nucleotide ofa SNP disclosed herein, is a composition that is specificallycontemplated by the present invention). Thus, reagents that bind to anucleic acid molecule in a region adjacent to a SNP site and that areused for assaying the SNP site, even though the bound sequences do notnecessarily include the SNP site itself, are also contemplated by thepresent invention.

SNP Detection Kits and Systems

A person skilled in the art will recognize that, based on the SNP andassociated sequence information disclosed herein, detection reagents canbe developed and used to assay any SNP of the present inventionindividually or in combination, and such detection reagents can bereadily incorporated into one of the established kit or system formatswhich are well known in the art. The terms “kits” and “systems”, as usedherein in the context of SNP detection reagents, are intended to referto such things as combinations of multiple SNP detection reagents, orone or more SNP detection reagents in combination with one or more othertypes of elements or components (e.g., other types of biochemicalreagents, containers, packages such as packaging intended for commercialsale, substrates to which SNP detection reagents are attached,electronic hardware components, etc.). Accordingly, the presentinvention further provides SNP detection kits and systems, including butnot limited to, packaged probe and primer sets (e.g., TaqManprobe/primer sets), arrays/microarrays of nucleic acid molecules, andbeads that contain one or more probes, primers, or other detectionreagents for detecting one or more SNPs of the present invention. Thekits/systems can optionally include various electronic hardwarecomponents; for example, arrays (“DNA chips”) and microfluidic systems(“lab-on-a-chip” systems) provided by various manufacturers typicallycomprise hardware components. Other kits/systems (e.g., probe/primersets) may not include electronic hardware components, but may becomprised of, for example, one or more SNP detection reagents (alongwith, optionally, other biochemical reagents) packaged in one or morecontainers.

In some embodiments, a SNP detection kit typically contains one or moredetection reagents and other components (e.g., a buffer, enzymes such asDNA polymerases or ligases, chain extension nucleotides such asdeoxynucleotide triphosphates, and in the case of Sanger-type DNAsequencing reactions, chain terminating nucleotides, positive controlsequences, negative control sequences, and the like) necessary to carryout an assay or reaction, such as amplification and/or detection of aSNP-containing nucleic acid molecule. A kit may further contain meansfor determining the amount of a target nucleic acid, and means forcomparing the amount with a standard, and can comprise instructions forusing the kit to detect the SNP-containing nucleic acid molecule ofinterest. In one embodiment of the present invention, kits are providedwhich contain the necessary reagents to carry out one or more assays todetect one or more SNPs disclosed herein. In a preferred embodiment ofthe present invention, SNP detection kits/systems are in the form ofnucleic acid arrays, or compartmentalized kits, includingmicrofluidic/lab-on-a-chip systems.

SNP detection kits/systems may contain, for example, one or more probes,or pairs of probes, that hybridize to a nucleic acid molecule at or neareach target SNP position. Multiple pairs of allele-specific probes maybe included in the kit/system to simultaneously assay large numbers ofSNPs, at least one of which is a SNP of the present invention. In somekits/systems, the allele-specific probes are immobilized to a substratesuch as an array or bead. For example, the same substrate can compriseallele-specific probes for detecting at least 1; 10; 100; 1000; 10,000;100,000 (or any other number in-between) or substantially all of theSNPs shown in Table 1 and/or Table 2.

The terms “arrays”, “microarrays”, and “DNA chips” are used hereininterchangeably to refer to an array of distinct polynucleotides affixedto a substrate, such as glass, plastic, paper, nylon or other type ofmembrane, filter, chip, or any other suitable solid support. Thepolynucleotides can be synthesized directly on the substrate, orsynthesized separate from the substrate and then affixed to thesubstrate. In one embodiment, the microarray is prepared and usedaccording to the methods described in U.S. Pat. No. 5,837,832, Chee etal., PCT application WO95/11995 (Chee et al.), Lockhart, D. J. et al.(1996; Nat. Biotech. 14: 1675-1680) and Schena, M. et al. (1996; Proc.Natl. Acad. Sci. 93: 10614-10619), all of which are incorporated hereinin their entirety by reference. In other embodiments, such arrays areproduced by the methods described by Brown et al., U.S. Pat. No.5,807,522.

Nucleic acid arrays are reviewed in the following references: Zammatteoet al., “New chips for molecular biology and diagnostics”, BiotechnolAnnu Rev. 2002; 8:85-101; Sosnowski et al., “Active microelectronicarray system for DNA hybridization, genotyping and pharmacogenomicapplications”, Psychiatr Genet. 2002 December; 12(4):181-92; Heller,“DNA microarray technology: devices, systems, and applications”, AnnuRev Biomed Eng. 2002; 4:129-53. Epub 2002 Mar. 22; Kolchinsky et al.,“Analysis of SNPs and other genomic variations using gel-based chips”,Hum Mutat. 2002 April; 19(4):343-60; and McGall et al., “High-densitygenechip oligonucleotide probe arrays”, Adv Biochem Eng Biotechnol.2002; 77:21-42.

Any number of probes, such as allele-specific probes, may be implementedin an array, and each probe or pair of probes can hybridize to adifferent SNP position. In the case of polynucleotide probes, they canbe synthesized at designated areas (or synthesized separately and thenaffixed to designated areas) on a substrate using a light-directedchemical process. Each DNA chip can contain, for example, thousands tomillions of individual synthetic polynucleotide probes arranged in agrid-like pattern and miniaturized (e.g., to the size of a dime).Preferably, probes are attached to a solid support in an ordered,addressable array.

A microarray can be composed of a large number of unique,single-stranded polynucleotides, usually either synthetic antisensepolynucleotides or fragments of cDNAs, fixed to a solid support. Typicalpolynucleotides are preferably about 6-60 nucleotides in length, morepreferably about 15-30 nucleotides in length, and most preferably about18-25 nucleotides in length. For certain types of microarrays or otherdetection kits/systems, it may be preferable to use oligonucleotidesthat are only about 7-20 nucleotides in length. In other types ofarrays, such as arrays used in conjunction with chemiluminescentdetection technology, preferred probe lengths can be, for example, about15-80 nucleotides in length, preferably about 50-70 nucleotides inlength, more preferably about 55-65 nucleotides in length, and mostpreferably about 60 nucleotides in length. The microarray or detectionkit can contain polynucleotides that cover the known 5′ or 3′ sequenceof a gene/transcript or target SNP site, sequential polynucleotides thatcover the full-length sequence of a gene/transcript; or uniquepolynucleotides selected from particular areas along the length of atarget gene/transcript sequence, particularly areas corresponding to oneor more SNPs disclosed in Table 1 and/or Table 2. Polynucleotides usedin the microarray or detection kit can be specific to a SNP or SNPs ofinterest (e.g., specific to a particular SNP allele at a target SNPsite, or specific to particular SNP alleles at multiple different SNPsites), or specific to a polymorphic gene/transcript orgenes/transcripts of interest.

Hybridization assays based on polynucleotide arrays rely on thedifferences in hybridization stability of the probes to perfectlymatched and mismatched target sequence variants. For SNP genotyping, itis generally preferable that stringency conditions used in hybridizationassays are high enough such that nucleic acid molecules that differ fromone another at as little as a single SNP position can be differentiated(e.g., typical SNP hybridization assays are designed so thathybridization will occur only if one particular nucleotide is present ata SNP position, but will not occur if an alternative nucleotide ispresent at that SNP position). Such high stringency conditions may bepreferable when using, for example, nucleic acid arrays ofallele-specific probes for SNP detection. Such high stringencyconditions are described in the preceding section, and are well known tothose skilled in the art and can be found in, for example, CurrentProtocols in Molecular Biology, John Wiley & Sons, N.Y. (1989),6.3.1-6.3.6.

In other embodiments, the arrays are used in conjunction withchemiluminescent detection technology. The following patents and patentapplications, which are all hereby incorporated by reference, provideadditional information pertaining to chemiluminescent detection: U.S.patent application Ser. Nos. 10/620,332 and 10/620,333 describechemiluminescent approaches for microarray detection; U.S. Pat. Nos.6,124,478, 6,107,024, 5,994,073, 5,981,768, 5,871,938, 5,843,681,5,800,999, and 5,773,628 describe methods and compositions of dioxetanefor performing chemiluminescent detection; and U.S. publishedapplication US2002/0110828 discloses methods and compositions formicroarray controls.

In one embodiment of the invention, a nucleic acid array can comprise anarray of probes of about 15-25 nucleotides in length. In furtherembodiments, a nucleic acid array can comprise any number of probes, inwhich at least one probe is capable of detecting one or more SNPsdisclosed in Table 1 and/or Table 2, and/or at least one probe comprisesa fragment of one of the sequences selected from the group consisting ofthose disclosed in Table 1, Table 2, the Sequence Listing, and sequencescomplementary thereto, said fragment comprising at least about 8consecutive nucleotides, preferably 10, 12, 15, 16, 18, 20, morepreferably 22, 25, 30, 40, 47, 50, 55, 60, 65, 70, 80, 90, 100, or moreconsecutive nucleotides (or any other number in-between) and containing(or being complementary to) a novel SNP allele disclosed in Table 1and/or Table 2. In some embodiments, the nucleotide complementary to theSNP site is within 5, 4, 3, 2, or 1 nucleotide from the center of theprobe, more preferably at the center of said probe.

A polynucleotide probe can be synthesized on the surface of thesubstrate by using a chemical coupling procedure and an ink jetapplication apparatus, as described in PCT application WO95/251116(Baldeschweiler et al.) which is incorporated herein in its entirety byreference. In another aspect, a “gridded” array analogous to a dot (orslot) blot may be used to arrange and link cDNA fragments oroligonucleotides to the surface of a substrate using a vacuum system,thermal, UV, mechanical or chemical bonding procedures. An array, suchas those described above, may be produced by hand or by using availabledevices (slot blot or dot blot apparatus), materials (any suitable solidsupport), and machines (including robotic instruments), and may contain8, 24, 96, 384, 1536, 6144 or more polynucleotides, or any other numberwhich lends itself to the efficient use of commercially availableinstrumentation.

Using such arrays or other kits/systems, the present invention providesmethods of identifying the SNPs disclosed herein in a test sample. Suchmethods typically involve incubating a test sample of nucleic acids withan array comprising one or more probes corresponding to at least one SNPposition of the present invention, and assaying for binding of a nucleicacid from the test sample with one or more of the probes. Conditions forincubating a SNP detection reagent (or a kit/system that employs one ormore such SNP detection reagents) with a test sample vary. Incubationconditions depend on such factors as the format employed in the assay,the detection methods employed, and the type and nature of the detectionreagents used in the assay. One skilled in the art will recognize thatany one of the commonly available hybridization, amplification and arrayassay formats can readily be adapted to detect the SNPs disclosedherein.

A SNP detection kit/system of the present invention may includecomponents that are used to prepare nucleic acids from a test sample forthe subsequent amplification and/or detection of a SNP-containingnucleic acid molecule. Such sample preparation components can be used toproduce nucleic acid extracts (including DNA and/or RNA), proteins ormembrane extracts from any bodily fluids (such as blood, serum, plasma,urine, saliva, phlegm, gastric juices, semen, tears, sweat, etc.), skin,hair, cells (especially nucleated cells), biopsies, buccal swabs ortissue specimens. The test samples used in the above-described methodswill vary based on such factors as the assay format, nature of thedetection method, and the specific tissues, cells or extracts used asthe test sample to be assayed. Methods of preparing nucleic acids,proteins, and cell extracts are well known in the art and can be readilyadapted to obtain a sample that is compatible with the system utilized.Automated sample preparation systems for extracting nucleic acids from atest sample are commercially available, and examples are Qiagen'sBioRobot 9600, Applied Biosystems' PRISM™ 6700 sample preparationsystem, and Roche Molecular Systems' COBAS AmpliPrep System.

Another form of kit contemplated by the present invention is acompartmentalized kit. A compartmentalized kit includes any kit in whichreagents are contained in separate containers. Such containers include,for example, small glass containers, plastic containers, strips ofplastic, glass or paper, or arraying material such as silica. Suchcontainers allow one to efficiently transfer reagents from onecompartment to another compartment such that the test samples andreagents are not cross-contaminated, or from one container to anothervessel not included in the kit, and the agents or solutions of eachcontainer can be added in a quantitative fashion from one compartment toanother or to another vessel. Such containers may include, for example,one or more containers which will accept the test sample, one or morecontainers which contain at least one probe or other SNP detectionreagent for detecting one or more SNPs of the present invention, one ormore containers which contain wash reagents (such as phosphate bufferedsaline, Tris-buffers, etc.), and one or more containers which containthe reagents used to reveal the presence of the bound probe or other SNPdetection reagents. The kit can optionally further comprise compartmentsand/or reagents for, for example, nucleic acid amplification or otherenzymatic reactions such as primer extension reactions, hybridization,ligation, electrophoresis (preferably capillary electrophoresis), massspectrometry, and/or laser-induced fluorescent detection. The kit mayalso include instructions for using the kit. Exemplary compartmentalizedkits include microfluidic devices known in the art (see, e.g., Weigl etal., “Lab-on-a-chip for drug development”, Adv Drug Deliv Rev. 2003 Feb.24; 55(3):349-77). In such microfluidic devices, the containers may bereferred to as, for example, microfluidic “compartments”, “chambers”, or“channels”.

Microfluidic devices, which may also be referred to as “lab-on-a-chip”systems, biomedical micro-electro-mechanical systems (bioMEMs), ormulticomponent integrated systems, are exemplary kits/systems of thepresent invention for analyzing SNPs. Such systems miniaturize andcompartmentalize processes such as probe/target hybridization, nucleicacid amplification, and capillary electrophoresis reactions in a singlefunctional device. Such microfluidic devices typically utilize detectionreagents in at least one aspect of the system, and such detectionreagents may be used to detect one or more SNPs of the presentinvention. One example of a microfluidic system is disclosed in U.S.Pat. No. 5,589,136, which describes the integration of PCR amplificationand capillary electrophoresis in chips. Exemplary microfluidic systemscomprise a pattern of microchannels designed onto a glass, silicon,quartz, or plastic wafer included on a microchip. The movements of thesamples may be controlled by electric, electroosmotic or hydrostaticforces applied across different areas of the microchip to createfunctional microscopic valves and pumps with no moving parts. Varyingthe voltage can be used as a means to control the liquid flow atintersections between the micro-machined channels and to change theliquid flow rate for pumping across different sections of the microchip.See, for example, U.S. Pat. Nos. 6,153,073, Dubrow et al., and6,156,181, Parce et al.

For genotyping SNPs, an exemplary microfluidic system may integrate, forexample, nucleic acid amplification, primer extension, capillaryelectrophoresis, and a detection method such as laser inducedfluorescence detection. In a first step of an exemplary process forusing such an exemplary system, nucleic acid samples are amplified,preferably by PCR. Then, the amplification products are subjected toautomated primer extension reactions using ddNTPs (specific fluorescencefor each ddNTP) and the appropriate oligonucleotide primers to carry outprimer extension reactions which hybridize just upstream of the targetedSNP. Once the extension at the 3′ end is completed, the primers areseparated from the unincorporated fluorescent ddNTPs by capillaryelectrophoresis. The separation medium used in capillary electrophoresiscan be, for example, polyacrylamide, polyethyleneglycol or dextran. Theincorporated ddNTPs in the single nucleotide primer extension productsare identified by laser-induced fluorescence detection. Such anexemplary microchip can be used to process, for example, at least 96 to384 samples, or more, in parallel.

Uses of Nucleic Acid Molecules

The nucleic acid molecules of the present invention have a variety ofuses, especially in the diagnosis and treatment of psoriasis and relatedpathologies. For example, the nucleic acid molecules are useful ashybridization probes, such as for genotyping SNPs in messenger RNA,transcript, cDNA, genomic DNA, amplified DNA or other nucleic acidmolecules, and for isolating full-length cDNA and genomic clonesencoding the variant peptides disclosed in Table 1 as well as theirorthologs.

A probe can hybridize to any nucleotide sequence along the entire lengthof a nucleic acid molecule provided in Table 1 and/or Table 2.Preferably, a probe of the present invention hybridizes to a region of atarget sequence that encompasses a SNP position indicated in Table 1and/or Table 2. More preferably, a probe hybridizes to a SNP-containingtarget sequence in a sequence-specific manner such that it distinguishesthe target sequence from other nucleotide sequences which vary from thetarget sequence only by which nucleotide is present at the SNP site.Such a probe is particularly useful for detecting the presence of aSNP-containing nucleic acid in a test sample, or for determining whichnucleotide (allele) is present at a particular SNP site (i.e.,genotyping the SNP site).

A nucleic acid hybridization probe may be used for determining thepresence, level, form, and/or distribution of nucleic acid expression.The nucleic acid whose level is determined can be DNA or RNA.Accordingly, probes specific for the SNPs described herein can be usedto assess the presence, expression and/or gene copy number in a givencell, tissue, or organism. These uses are relevant for diagnosis ofdisorders involving an increase or decrease in gene expression relativeto normal levels. In vitro techniques for detection of mRNA include, forexample, Northern blot hybridizations and in situ hybridizations. Invitro techniques for detecting DNA include Southern blot hybridizationsand in situ hybridizations (Sambrook and Russell, 2000, MolecularCloning: A Laboratory Manual, Cold Spring Harbor Press, Cold SpringHarbor, N.Y.).

Probes can be used as part of a diagnostic test kit for identifyingcells or tissues in which a variant protein is expressed, such as bymeasuring the level of a variant protein-encoding nucleic acid (e.g.,mRNA) in a sample of cells from a subject or determining if apolynucleotide contains a SNP of interest.

Thus, the nucleic acid molecules of the invention can be used ashybridization probes to detect the SNPs disclosed herein, therebydetermining whether an individual with the polymorphisms is at risk forpsoriasis and related pathologies or has developed early stagepsoriasis. Detection of a SNP associated with a disease phenotypeprovides a diagnostic tool for an active disease and/or geneticpredisposition to the disease.

Furthermore, the nucleic acid molecules of the invention are thereforeuseful for detecting a gene (gene information is disclosed in Table 2,for example) which contains a SNP disclosed herein and/or products ofsuch genes, such as expressed mRNA transcript molecules (transcriptinformation is disclosed in Table 1, for example), and are thus usefulfor detecting gene expression. The nucleic acid molecules can optionallybe implemented in, for example, an array or kit format for use indetecting gene expression.

The nucleic acid molecules of the invention are also useful as primersto amplify any given region of a nucleic acid molecule, particularly aregion containing a SNP identified in Table 1 and/or Table 2.

The nucleic acid molecules of the invention are also useful forconstructing recombinant vectors (described in greater detail below).Such vectors include expression vectors that express a portion of, orall of, any of the variant peptide sequences provided in Table 1.Vectors also include insertion vectors, used to integrate into anothernucleic acid molecule sequence, such as into the cellular genome, toalter in situ expression of a gene and/or gene product. For example, anendogenous coding sequence can be replaced via homologous recombinationwith all or part of the coding region containing one or morespecifically introduced SNPs.

The nucleic acid molecules of the invention are also useful forexpressing antigenic portions of the variant proteins, particularlyantigenic portions that contain a variant amino acid sequence (e.g., anamino acid substitution) caused by a SNP disclosed in Table 1 and/orTable 2.

The nucleic acid molecules of the invention are also useful forconstructing vectors containing a gene regulatory region of the nucleicacid molecules of the present invention.

The nucleic acid molecules of the invention are also useful fordesigning ribozymes corresponding to all, or a part, of an mRNA moleculeexpressed from a SNP-containing nucleic acid molecule described herein.

The nucleic acid molecules of the invention are also useful forconstructing host cells expressing a part, or all, of the nucleic acidmolecules and variant peptides.

The nucleic acid molecules of the invention are also useful forconstructing transgenic animals expressing all, or a part, of thenucleic acid molecules and variant peptides. The production ofrecombinant cells and transgenic animals having nucleic acid moleculeswhich contain the SNPs disclosed in Table 1 and/or Table 2 allow, forexample, effective clinical design of treatment compounds and dosageregimens.

The nucleic acid molecules of the invention are also useful in assaysfor drug screening to identify compounds that, for example, modulatenucleic acid expression.

The nucleic acid molecules of the invention are also useful in genetherapy in patients whose cells have aberrant gene expression. Thus,recombinant cells, which include a patient's cells that have beenengineered ex vivo and returned to the patient, can be introduced intoan individual where the recombinant cells produce the desired protein totreat the individual.

SNP Genotyping Methods

The process of determining which specific nucleotide (i.e., allele) ispresent at each of one or more SNP positions, such as a SNP position ina nucleic acid molecule disclosed in Table 1 and/or Table 2, is referredto as SNP genotyping. The present invention provides methods of SNPgenotyping, such as for use in screening for psoriasis or relatedpathologies, or determining predisposition thereto, or determiningresponsiveness to a form of treatment, or in genome mapping or SNPassociation analysis, etc.

Nucleic acid samples can be genotyped to determine which allele(s)is/are present at any given genetic region (e.g., SNP position) ofinterest by methods well known in the art. The neighboring sequence canbe used to design SNP detection reagents such as oligonucleotide probes,which may optionally be implemented in a kit format. Exemplary SNPgenotyping methods are described in Chen et al., “Single nucleotidepolymorphism genotyping: biochemistry, protocol, cost and throughput”,Pharmacogenomics J. 2003; 3(2):77-96; Kwok et al., “Detection of singlenucleotide polymorphisms”, Curr Issues Mol Biol. 2003 April; 5(2):43-60;Shi, “Technologies for individual genotyping: detection of geneticpolymorphisms in drug targets and disease genes”, Am J Pharmacogenomics.2002; 2(3):197-205; and Kwok, “Methods for genotyping single nucleotidepolymorphisms”, Annu Rev Genomics Hum Genet 2001; 2:235-58. Exemplarytechniques for high-throughput SNP genotyping are described inMarnellos, “High-throughput SNP analysis for genetic associationstudies”, Curr Opin Drug Discov Devel. 2003 May; 6(3):317-21. Common SNPgenotyping methods include, but are not limited to, TaqMan assays,molecular beacon assays, nucleic acid arrays, allele-specific primerextension, allele-specific PCR, arrayed primer extension, homogeneousprimer extension assays, primer extension with detection by massspectrometry, pyrosequencing, multiplex primer extension sorted ongenetic arrays, ligation with rolling circle amplification, homogeneousligation, OLA (U.S. Pat. No. 4,988,167), multiplex ligation reactionsorted on genetic arrays, restriction-fragment length polymorphism,single base extension-tag assays, and the Invader assay. Such methodsmay be used in combination with detection mechanisms such as, forexample, luminescence or chemiluminescence detection, fluorescencedetection, time-resolved fluorescence detection, fluorescence resonanceenergy transfer, fluorescence polarization, mass spectrometry, andelectrical detection.

Various methods for detecting polymorphisms include, but are not limitedto, methods in which protection from cleavage agents is used to detectmismatched bases in RNA/RNA or RNA/DNA duplexes (Myers et al., Science230:1242 (1985); Cotton et al., PNAS 85:4397 (1988); and Saleeba et al.,Meth. Enzymol. 217:286-295 (1992)), comparison of the electrophoreticmobility of variant and wild type nucleic acid molecules (Orita et al.,PNAS 86:2766 (1989); Cotton et al., Mutat. Res. 285:125-144 (1993); andHayashi et al., Genet. Anal. Tech. Appl. 9:73-79 (1992)), and assayingthe movement of polymorphic or wild-type fragments in polyacrylamidegels containing a gradient of denaturant using denaturing gradient gelelectrophoresis (DGGE) (Myers et al., Nature 313:495 (1985)). Sequencevariations at specific locations can also be assessed by nucleaseprotection assays such as RNase and S1 protection or chemical cleavagemethods.

In a preferred embodiment, SNP genotyping is performed using the TaqManassay, which is also known as the 5′ nuclease assay (U.S. Pat. Nos.5,210,015 and 5,538,848). The TaqMan assay detects the accumulation of aspecific amplified product during PCR. The TaqMan assay utilizes anoligonucleotide probe labeled with a fluorescent reporter dye and aquencher dye. The reporter dye is excited by irradiation at anappropriate wavelength, it transfers energy to the quencher dye in thesame probe via a process called fluorescence resonance energy transfer(FRET). When attached to the probe, the excited reporter dye does notemit a signal. The proximity of the quencher dye to the reporter dye inthe intact probe maintains a reduced fluorescence for the reporter. Thereporter dye and quencher dye may be at the 5′ most and the 3′ mostends, respectively, or vice versa. Alternatively, the reporter dye maybe at the 5′ or 3′ most end while the quencher dye is attached to aninternal nucleotide, or vice versa. In yet another embodiment, both thereporter and the quencher may be attached to internal nucleotides at adistance from each other such that fluorescence of the reporter isreduced.

During PCR, the 5′ nuclease activity of DNA polymerase cleaves theprobe, thereby separating the reporter dye and the quencher dye andresulting in increased fluorescence of the reporter. Accumulation of PCRproduct is detected directly by monitoring the increase in fluorescenceof the reporter dye. The DNA polymerase cleaves the probe between thereporter dye and the quencher dye only if the probe hybridizes to thetarget SNP-containing template which is amplified during PCR, and theprobe is designed to hybridize to the target SNP site only if aparticular SNP allele is present.

Preferred TaqMan primer and probe sequences can readily be determinedusing the SNP and associated nucleic acid sequence information providedherein. A number of computer programs, such as Primer Express (AppliedBiosystems, Foster City, Calif.), can be used to rapidly obtain optimalprimer/probe sets. It will be apparent to one of skill in the art thatsuch primers and probes for detecting the SNPs of the present inventionare useful in diagnostic assays for psoriasis and related pathologies,and can be readily incorporated into a kit format. The present inventionalso includes modifications of the Taqman assay well known in the artsuch as the use of Molecular Beacon probes (U.S. Pat. Nos. 5,118,801 and5,312,728) and other variant formats (U.S. Pat. Nos. 5,866,336 and6,117,635).

Another preferred method for genotyping the SNPs of the presentinvention is the use of two oligonucleotide probes in an OLA (see, e.g.,U.S. Pat. No. 4,988,617). In this method, one probe hybridizes to asegment of a target nucleic acid with its 3′ most end aligned with theSNP site. A second probe hybridizes to an adjacent segment of the targetnucleic acid molecule directly 3′ to the first probe. The two juxtaposedprobes hybridize to the target nucleic acid molecule, and are ligated inthe presence of a linking agent such as a ligase if there is perfectcomplementarity between the 3′ most nucleotide of the first probe withthe SNP site. If there is a mismatch, ligation would not occur. Afterthe reaction, the ligated probes are separated from the target nucleicacid molecule, and detected as indicators of the presence of a SNP.

The following patents, patent applications, and published internationalpatent applications, which are all hereby incorporated by reference,provide additional information pertaining to techniques for carrying outvarious types of OLA: U.S. Pat. Nos. 6,027,889, 6,268,148, 5494810,5830711, and 6054564 describe OLA strategies for performing SNPdetection; WO 97/31256 and WO 00/56927 describe OLA strategies forperforming SNP detection using universal arrays, wherein a zipcodesequence can be introduced into one of the hybridization probes, and theresulting product, or amplified product, hybridized to a universal zipcode array; U.S. application US01/17329 (and Ser. No. 09/584,905)describes OLA (or LDR) followed by PCR, wherein zipcodes areincorporated into OLA probes, and amplified PCR products are determinedby electrophoretic or universal zipcode array readout; U.S. applications60/427,818, 60/445,636, and 60/445,494 describe SNPlex methods andsoftware for multiplexed SNP detection using OLA followed by PCR,wherein zipcodes are incorporated into OLA probes, and amplified PCRproducts are hybridized with a zipchute reagent, and the identity of theSNP determined from electrophoretic readout of the zipchute. In someembodiments, OLA is carried out prior to PCR (or another method ofnucleic acid amplification). In other embodiments, PCR (or anothermethod of nucleic acid amplification) is carried out prior to OLA.

Another method for SNP genotyping is based on mass spectrometry. Massspectrometry takes advantage of the unique mass of each of the fournucleotides of DNA. SNPs can be unambiguously genotyped by massspectrometry by measuring the differences in the mass of nucleic acidshaving alternative SNP alleles. MALDI-TOF (Matrix Assisted LaserDesorption Ionization—Time of Flight) mass spectrometry technology ispreferred for extremely precise determinations of molecular mass, suchas SNPs. Numerous approaches to SNP analysis have been developed basedon mass spectrometry. Preferred mass spectrometry-based methods of SNPgenotyping include primer extension assays, which can also be utilizedin combination with other approaches, such as traditional gel-basedformats and microarrays.

Typically, the primer extension assay involves designing and annealing aprimer to a template PCR amplicon upstream (5′) from a target SNPposition. A mix of dideoxynucleotide triphosphates (ddNTPs) and/ordeoxynucleotide triphosphates (dNTPs) are added to a reaction mixturecontaining template (e.g., a SNP-containing nucleic acid molecule whichhas typically been amplified, such as by PCR), primer, and DNApolymerase. Extension of the primer terminates at the first position inthe template where a nucleotide complementary to one of the ddNTPs inthe mix occurs. The primer can be either immediately adjacent (i.e., thenucleotide at the 3′ end of the primer hybridizes to the nucleotide nextto the target SNP site) or two or more nucleotides removed from the SNPposition. If the primer is several nucleotides removed from the targetSNP position, the only limitation is that the template sequence betweenthe 3′ end of the primer and the SNP position cannot contain anucleotide of the same type as the one to be detected, or this willcause premature termination of the extension primer. Alternatively, ifall four ddNTPs alone, with no dNTPs, are added to the reaction mixture,the primer will always be extended by only one nucleotide, correspondingto the target SNP position. In this instance, primers are designed tobind one nucleotide upstream from the SNP position (i.e., the nucleotideat the 3′ end of the primer hybridizes to the nucleotide that isimmediately adjacent to the target SNP site on the 5′ side of the targetSNP site). Extension by only one nucleotide is preferable, as itminimizes the overall mass of the extended primer, thereby increasingthe resolution of mass differences between alternative SNP nucleotides.Furthermore, mass-tagged ddNTPs can be employed in the primer extensionreactions in place of unmodified ddNTPs. This increases the massdifference between primers extended with these ddNTPs, thereby providingincreased sensitivity and accuracy, and is particularly useful fortyping heterozygous base positions. Mass-tagging also alleviates theneed for intensive sample-preparation procedures and decreases thenecessary resolving power of the mass spectrometer.

The extended primers can then be purified and analyzed by MALDI-TOF massspectrometry to determine the identity of the nucleotide present at thetarget SNP position. In one method of analysis, the products from theprimer extension reaction are combined with light absorbing crystalsthat form a matrix. The matrix is then hit with an energy source such asa laser to ionize and desorb the nucleic acid molecules into thegas-phase. The ionized molecules are then ejected into a flight tube andaccelerated down the tube towards a detector. The time between theionization event, such as a laser pulse, and collision of the moleculewith the detector is the time of flight of that molecule. The time offlight is precisely correlated with the mass-to-charge ratio (m/z) ofthe ionized molecule. Ions with smaller m/z travel down the tube fasterthan ions with larger m/z and therefore the lighter ions reach thedetector before the heavier ions. The time-of-flight is then convertedinto a corresponding, and highly precise, m/z. In this manner, SNPs canbe identified based on the slight differences in mass, and thecorresponding time of flight differences, inherent in nucleic acidmolecules having different nucleotides at a single base position. Forfurther information regarding the use of primer extension assays inconjunction with MALDI-TOF mass spectrometry for SNP genotyping, see,e.g., Wise et al., “A standard protocol for single nucleotide primerextension in the human genome using matrix-assisted laserdesorption/ionization time-of-flight mass spectrometry”, Rapid CommunMass Spectrom. 2003; 17(11):1195-202.

The following references provide further information describing massspectrometry-based methods for SNP genotyping: Bocker, “SNP and mutationdiscovery using base-specific cleavage and MALDI-TOF mass spectrometry”,Bioinformatics. 2003 July; 19 Suppl 1:144-153; Storm et al., “MALDI-TOFmass spectrometry-based SNP genotyping”, Methods Mol Biol. 2003;212:241-62; Jurinke et al., “The use of Mass ARRAY technology for highthroughput genotyping”, Adv Biochem Eng Biotechnol. 2002; 77:57-74; andJurinke et al., “Automated genotyping using the DNA MassArraytechnology”, Methods Mol Biol. 2002; 187:179-92.

SNPs can also be scored by direct DNA sequencing. A variety of automatedsequencing procedures can be utilized ((1995) Biotechniques 19:448),including sequencing by mass spectrometry (see, e.g., PCT InternationalPublication No. WO94/16101; Cohen et al., Adv. Chromatogr. 36:127-162(1996); and Griffin et al., Appl. Biochem. Biotechnol. 38:147-159(1993)). The nucleic acid sequences of the present invention enable oneof ordinary skill in the art to readily design sequencing primers forsuch automated sequencing procedures. Commercial instrumentation, suchas the Applied Biosystems 377, 3100, 3700, 3730, and 3730×1 DNAAnalyzers (Foster City, Calif.), is commonly used in the art forautomated sequencing.

Other methods that can be used to genotype the SNPs of the presentinvention include single-strand conformational polymorphism (SSCP), anddenaturing gradient gel electrophoresis (DGGE) (Myers et al., Nature313:495 (1985)). SSCP identifies base differences by alteration inelectrophoretic migration of single stranded PCR products, as describedin Orita et al., Proc. Nat. Acad. Single-stranded PCR products can begenerated by heating or otherwise denaturing double stranded PCRproducts. Single-stranded nucleic acids may refold or form secondarystructures that are partially dependent on the base sequence. Thedifferent electrophoretic mobilities of single-stranded amplificationproducts are related to base-sequence differences at SNP positions. DGGEdifferentiates SNP alleles based on the different sequence-dependentstabilities and melting properties inherent in polymorphic DNA and thecorresponding differences in electrophoretic migration patterns in adenaturing gradient gel (Erlich, ed., PCR Technology, Principles andApplications for DNA Amplification, W.H. Freeman and Co, New York, 1992,Chapter 7).

Sequence-specific ribozymes (U.S. Pat. No. 5,498,531) can also be usedto score SNPs based on the development or loss of a ribozyme cleavagesite. Perfectly matched sequences can be distinguished from mismatchedsequences by nuclease cleavage digestion assays or by differences inmelting temperature. If the SNP affects a restriction enzyme cleavagesite, the SNP can be identified by alterations in restriction enzymedigestion patterns, and the corresponding changes in nucleic acidfragment lengths determined by gel electrophoresis

SNP genotyping can include the steps of, for example, collecting abiological sample from a human subject (e.g., sample of tissues, cells,fluids, secretions, etc.), isolating nucleic acids (e.g., genomic DNA,mRNA or both) from the cells of the sample, contacting the nucleic acidswith one or more primers which specifically hybridize to a region of theisolated nucleic acid containing a target SNP under conditions such thathybridization and amplification of the target nucleic acid regionoccurs, and determining the nucleotide present at the SNP position ofinterest, or, in some assays, detecting the presence or absence of anamplification product (assays can be designed so that hybridizationand/or amplification will only occur if a particular SNP allele ispresent or absent). In some assays, the size of the amplificationproduct is detected and compared to the length of a control sample; forexample, deletions and insertions can be detected by a change in size ofthe amplified product compared to a normal genotype.

SNP genotyping is useful for numerous practical applications, asdescribed below. Examples of such applications include, but are notlimited to, SNP-disease association analysis, disease predispositionscreening, disease diagnosis, disease prognosis, disease progressionmonitoring, determining therapeutic strategies based on an individual'sgenotype (“pharmacogenomics”), developing therapeutic agents based onSNP genotypes associated with a disease or likelihood of responding to adrug, stratifying a patient population for clinical trial for atreatment regimen, predicting the likelihood that an individual willexperience toxic side effects from a therapeutic agent, and humanidentification applications such as forensics.

Analysis of Genetic Association Between SNPs and Phenotypic Traits SNPgenotyping for disease diagnosis, disease predisposition screening,disease prognosis, determining drug responsiveness (pharmacogenomics),drug toxicity screening, and other uses described herein, typicallyrelies on initially establishing a genetic association between one ormore specific SNPs and the particular phenotypic traits of interest.

Different study designs may be used for genetic association studies(Modern Epidemiology, Lippincott Williams & Wilkins (1998), 609-622).Observational studies are most frequently carried out in which theresponse of the patients is not interfered with. The first type ofobservational study identifies a sample of persons in whom the suspectedcause of the disease is present and another sample of persons in whomthe suspected cause is absent, and then the frequency of development ofdisease in the two samples is compared. These sampled populations arecalled cohorts, and the study is a prospective study. The other type ofobservational study is case-control or a retrospective study. In typicalcase-control studies, samples are collected from individuals with thephenotype of interest (cases) such as certain manifestations of adisease, and from individuals without the phenotype (controls) in apopulation (target population) that conclusions are to be drawn from.Then the possible causes of the disease are investigatedretrospectively. As the time and costs of collecting samples incase-control studies are considerably less than those for prospectivestudies, case-control studies are the more commonly used study design ingenetic association studies, at least during the exploration anddiscovery stage.

In both types of observational studies, there may be potentialconfounding factors that should be taken into consideration. Confoundingfactors are those that are associated with both the real cause(s) of thedisease and the disease itself, and they include demographic informationsuch as age, gender, ethnicity as well as environmental factors. Whenconfounding factors are not matched in cases and controls in a study,and are not controlled properly, spurious association results can arise.If potential confounding factors are identified, they should becontrolled for by analysis methods explained below.

In a genetic association study, the cause of interest to be tested is acertain allele or a SNP or a combination of alleles or a haplotype fromseveral SNPs. Thus, tissue specimens (e.g., whole blood) from thesampled individuals may be collected and genomic DNA genotyped for theSNP(s) of interest. In addition to the phenotypic trait of interest,other information such as demographic (e.g., age, gender, ethnicity,etc.), clinical, and environmental information that may influence theoutcome of the trait can be collected to further characterize and definethe sample set. In many cases, these factors are known to be associatedwith diseases and/or SNP allele frequencies. There are likelygene-environment and/or gene-gene interactions as well. Analysis methodsto address gene-environment and gene-gene interactions (for example, theeffects of the presence of both susceptibility alleles at two differentgenes can be greater than the effects of the individual alleles at twogenes combined) are discussed below.

After all the relevant phenotypic and genotypic information has beenobtained, statistical analyses are carried out to determine if there isany significant correlation between the presence of an allele or agenotype with the phenotypic characteristics of an individual.Preferably, data inspection and cleaning are first performed beforecarrying out statistical tests for genetic association. Epidemiologicaland clinical data of the samples can be summarized by descriptivestatistics with tables and graphs. Data validation is preferablyperformed to check for data completion, inconsistent entries, andoutliers. Chi-squared tests and t-tests (Wilcoxon rank-sum tests ifdistributions are not normal) may then be used to check for significantdifferences between cases and controls for discrete and continuousvariables, respectively. To ensure genotyping quality, Hardy-Weinbergdisequilibrium tests can be performed on cases and controls separately.Significant deviation from Hardy-Weinberg equilibrium (HWE) in bothcases and controls for individual markers can be indicative ofgenotyping errors. If HWE is violated in a majority of markers, it isindicative of population substructure that should be furtherinvestigated. Moreover, Hardy-Weinberg disequilibrium in cases only canindicate genetic association of the markers with the disease (GeneticData Analysis, Weir B., Sinauer (1990)).

To test whether an allele of a single SNP is associated with the case orcontrol status of a phenotypic trait, one skilled in the art can compareallele frequencies in cases and controls. Standard chi-squared tests andFisher exact tests can be carried out on a 2×2 table (2 SNP alleles×2outcomes in the categorical trait of interest). To test whethergenotypes of a SNP are associated, chi-squared tests can be carried outon a 3×2 table (3 genotypes×2 outcomes). Score tests are also carriedout for genotypic association to contrast the three genotypicfrequencies (major homozygotes, heterozygotes and minor homozygotes) incases and controls, and to look for trends using 3 different modes ofinheritance, namely dominant (with contrast coefficients 2, −1, −1),additive (with contrast coefficients 1, 0, −1) and recessive (withcontrast coefficients 1, 1, −2). Odds ratios for minor versus majoralleles, and odds ratios for heterozygote and homozygote variants versusthe wild type genotypes are calculated with the desired confidencelimits, usually 95%.

In order to control for confounders and to test for interaction andeffect modifiers, stratified analyses may be performed using stratifiedfactors that are likely to be confounding, including demographicinformation such as age, ethnicity, and gender, or an interactingelement or effect modifier, such as a known major gene (e.g., APOE forAlzheimer's disease or HLA genes for autoimmune diseases), orenvironmental factors such as smoking in lung cancer. Stratifiedassociation tests may be carried out using Cochran-Mantel-Haenszel teststhat take into account the ordinal nature of genotypes with 0, 1, and 2variant alleles. Exact tests by StatXact may also be performed whencomputationally possible. Another way to adjust for confounding effectsand test for interactions is to perform stepwise multiple logisticregression analysis using statistical packages such as SAS or R.Logistic regression is a model-building technique in which the bestfitting and most parsimonious model is built to describe the relationbetween the dichotomous outcome (for instance, getting a certain diseaseor not) and a set of independent variables (for instance, genotypes ofdifferent associated genes, and the associated demographic andenvironmental factors). The most common model is one in which the logittransformation of the odds ratios is expressed as a linear combinationof the variables (main effects) and their cross-product terms(interactions) (Applied Logistic Regression, Hosmer and Lemeshow, Wiley(2000)). To test whether a certain variable or interaction issignificantly associated with the outcome, coefficients in the model arefirst estimated and then tested for statistical significance of theirdeparture from zero.

In addition to performing association tests one marker at a time,haplotype association analysis may also be performed to study a numberof markers that are closely linked together. Haplotype association testscan have better power than genotypic or allelic association tests whenthe tested markers are not the disease-causing mutations themselves butare in linkage disequilibrium with such mutations. The test will even bemore powerful if the disease is indeed caused by a combination ofalleles on a haplotype (e.g., APOE is a haplotype formed by 2 SNPs thatare very close to each other). In order to perform haplotype associationeffectively, marker-marker linkage disequilibrium measures, both D′ andR², are typically calculated for the markers within a gene to elucidatethe haplotype structure. Recent studies (Daly et al, Nature Genetics,29, 232-235, 2001) in linkage disequilibrium indicate that SNPs within agene are organized in block pattern, and a high degree of linkagedisequilibrium exists within blocks and very little linkagedisequilibrium exists between blocks. Haplotype association with thedisease status can be performed using such blocks once they have beenelucidated.

Haplotype association tests can be carried out in a similar fashion asthe allelic and genotypic association tests. Each haplotype in a gene isanalogous to an allele in a multi-allelic marker. One skilled in the artcan either compare the haplotype frequencies in cases and controls ortest genetic association with different pairs of haplotypes. It has beenproposed (Schaid et al, Am. J. Hum. Genet., 70, 425-434, 2002) thatscore tests can be done on haplotypes using the program “haplo.score”.In that method, haplotypes are first inferred by EM algorithm and scoretests are carried out with a generalized linear model (GLM) frameworkthat allows the adjustment of other factors.

An important decision in the performance of genetic association tests isthe determination of the significance level at which significantassociation can be declared when the p-value of the tests reaches thatlevel. In an exploratory analysis where positive hits will be followedup in subsequent confirmatory testing, an unadjusted p-value<0.2 (asignificance level on the lenient side), for example, may be used forgenerating hypotheses for significant association of a SNP with certainphenotypic characteristics of a disease. It is preferred that ap-value<0.05 (a significance level traditionally used in the art) isachieved in order for a SNP to be considered to have an association witha disease. It is more preferred that a p-value<0.01 (a significancelevel on the stringent side) is achieved for an association to bedeclared. When hits are followed up in confirmatory analyses in moresamples of the same source or in different samples from differentsources, adjustment for multiple testing will be performed as to avoidexcess number of hits while maintaining the experiment-wise error ratesat 0.05. While there are different methods to adjust for multipletesting to control for different kinds of error rates, a commonly usedbut rather conservative method is Bonferroni correction to control theexperiment-wise or family-wise error rate (Multiple comparisons andmultiple tests, Westfall et al, SAS Institute (1999)). Permutation teststo control for the false discovery rates, FDR, can be more powerful(Benjamini and Hochberg, Journal of the Royal Statistical Society,Series B 57, 1289-1300, 1995, Resampling-based Multiple Testing,Westfall and Young, Wiley (1993)). Such methods to control formultiplicity would be preferred when the tests are dependent andcontrolling for false discovery rates is sufficient as opposed tocontrolling for the experiment-wise error rates.

In replication studies using samples from different populations afterstatistically significant markers have been identified in theexploratory stage, meta-analyses can then be performed by combiningevidence of different studies (Modern Epidemiology, Lippincott Williams& Wilkins, 1998, 643-673). If available, association results known inthe art for the same SNPs can be included in the meta-analyses.

Since both genotyping and disease status classification can involveerrors, sensitivity analyses may be performed to see how odds ratios andp-values would change upon various estimates on genotyping and diseaseclassification error rates.

It has been well known that subpopulation-based sampling bias betweencases and controls can lead to spurious results in case-controlassociation studies (Ewens and Spielman, Am. J. Hum. Genet. 62, 450-458,1995) when prevalence of the disease is associated with differentsubpopulation groups. Such bias can also lead to a loss of statisticalpower in genetic association studies. To detect populationstratification, Pritchard and Rosenberg (Pritchard et al. Am. J. Hum.Gen. 1999, 65:220-228) suggested typing markers that are unlinked to thedisease and using results of association tests on those markers todetermine whether there is any population stratification. Whenstratification is detected, the genomic control (GC) method as proposedby Devlin and Roeder (Devlin et al. Biometrics 1999, 55:997-1004) can beused to adjust for the inflation of test statistics due to populationstratification. GC method is robust to changes in population structurelevels as well as being applicable to DNA pooling designs (Devlin et al.Genet. Epidem. 20001, 21:273-284).

While Pritchard's method recommended using 15-20 unlinked microsatellitemarkers, it suggested using more than 30 biallelic markers to get enoughpower to detect population stratification. For the GC method, it hasbeen shown (Bacanu et al. Am. J. Hum. Genet. 2000, 66:1933-1944) thatabout 60-70 biallelic markers are sufficient to estimate the inflationfactor for the test statistics due to population stratification. Hence,70 intergenic SNPs can be chosen in unlinked regions as indicated in agenome scan (Kehoe et al. Hum. Mol. Genet. 1999, 8:237-245).

Once individual risk factors, genetic or non-genetic, have been foundfor the predisposition to disease, the next step is to set up aclassification/prediction scheme to predict the category (for instance,disease or no-disease) that an individual will be in depending on hisgenotypes of associated SNPs and other non-genetic risk factors.Logistic regression for discrete trait and linear regression forcontinuous trait are standard techniques for such tasks (AppliedRegression Analysis, Draper and Smith, Wiley (1998)). Moreover, othertechniques can also be used for setting up classification. Suchtechniques include, but are not limited to, MART, CART, neural network,and discriminant analyses that are suitable for use in comparing theperformance of different methods (The Elements of Statistical Learning,Hastie, Tibshirani & Friedman, Springer (2002)).

Disease Diagnosis and Predisposition Screening

Information on association/correlation between genotypes anddisease-related phenotypes can be exploited in several ways. Forexample, in the case of a highly statistically significant associationbetween one or more SNPs with predisposition to a disease for whichtreatment is available, detection of such a genotype pattern in anindividual may justify immediate administration of treatment, or atleast the institution of regular monitoring of the individual. Detectionof the susceptibility alleles associated with serious disease in acouple contemplating having children may also be valuable to the couplein their reproductive decisions. In the case of a weaker but stillstatistically significant association between a SNP and a human disease,immediate therapeutic intervention or monitoring may not be justifiedafter detecting the susceptibility allele or SNP. Nevertheless, thesubject can be motivated to begin simple life-style changes (e.g., diet,exercise) that can be accomplished at little or no cost to theindividual but would confer potential benefits in reducing the risk ofdeveloping conditions for which that individual may have an increasedrisk by virtue of having the susceptibility allele(s).

The SNPs of the invention may contribute to psoriasis and relatedpathologies in an individual in different ways. Some polymorphisms occurwithin a protein coding sequence and contribute to disease phenotype byaffecting protein structure. Other polymorphisms occur in noncodingregions but may exert phenotypic effects indirectly via influence on,for example, replication, transcription, and/or translation. A singleSNP may affect more than one phenotypic trait. Likewise, a singlephenotypic trait may be affected by multiple SNPs in different genes.

As used herein, the terms “diagnose”, “diagnosis”, and “diagnostics”include, but are not limited to any of the following: detection ofpsoriasis that an individual may presently have,predisposition/susceptibility screening (i.e., determining the increasedrisk of an individual in developing psoriasis in the future, ordetermining whether an individual has a decreased risk of developingpsoriasis in the future, determining a particular type or subclass ofpsoriasis in an individual known to have psoriasis, confirming orreinforcing a previously made diagnosis of psoriasis, pharmacogenomicevaluation of an individual to determine which therapeutic strategy thatindividual is most likely to positively respond to or to predict whethera patient is likely to respond to a particular treatment, predictingwhether a patient is likely to experience toxic effects from aparticular treatment or therapeutic compound, and evaluating the futureprognosis of an individual having psoriasis. Such diagnostic uses arebased on the SNPs individually or in a unique combination or SNPhaplotypes of the present invention.

Haplotypes are particularly useful in that, for example, fewer SNPs canbe genotyped to determine if a particular genomic region harbors a locusthat influences a particular phenotype, such as in linkagedisequilibrium-based SNP association analysis.

Linkage disequilibrium (LD) refers to the co-inheritance of alleles(e.g., alternative nucleotides) at two or more different SNP sites atfrequencies greater than would be expected from the separate frequenciesof occurrence of each allele in a given population. The expectedfrequency of co-occurrence of two alleles that are inheritedindependently is the frequency of the first allele multiplied by thefrequency of the second allele. Alleles that co-occur at expectedfrequencies are said to be in “linkage equilibrium”. In contrast, LDrefers to any non-random genetic association between allele(s) at two ormore different SNP sites, which is generally due to the physicalproximity of the two loci along a chromosome. LD can occur when two ormore SNPs sites are in close physical proximity to each other on a givenchromosome and therefore alleles at these SNP sites will tend to remainunseparated for multiple generations with the consequence that aparticular nucleotide (allele) at one SNP site will show a non-randomassociation with a particular nucleotide (allele) at a different SNPsite located nearby. Hence, genotyping one of the SNP sites will givealmost the same information as genotyping the other SNP site that is inLD.

Various degrees of LD can be encountered between two or more SNPs withthe result being that some SNPs are more closely associated (i.e., instronger LD) than others. Furthermore, the physical distance over whichLD extends along a chromosome differs between different regions of thegenome, and therefore the degree of physical separation between two ormore SNP sites necessary for LD to occur can differ between differentregions of the genome.

For diagnostic purposes and similar uses, if a particular SNP site isfound to be useful for diagnosing psoriasis and related pathologies(e.g., has a significant statistical association with the conditionand/or is recognized as a causative polymorphism for the condition),then the skilled artisan would recognize that other SNP sites which arein LD with this SNP site would also be useful for diagnosing thecondition. Thus, polymorphisms (e.g., SNPs and/or haplotypes) that arenot the actual disease-causing (causative) polymorphisms, but are in LDwith such causative polymorphisms, are also useful. In such instances,the genotype of the polymorphism(s) that is/are in LD with the causativepolymorphism is predictive of the genotype of the causative polymorphismand, consequently, predictive of the phenotype (e.g., psoriasis) that isinfluenced by the causative SNP(s). Therefore, polymorphic markers thatare in LD with causative polymorphisms are useful as diagnostic markers,and are particularly useful when the actual causative polymorphism(s)is/are unknown.

Examples of polymorphisms that can be in LD with one or more causativepolymorphisms (and/or in LD with one or more polymorphisms that have asignificant statistical association with a condition) and thereforeuseful for diagnosing the same condition that the causative/associatedSNP(s) is used to diagnose, include, for example, other SNPs in the samegene, protein-coding, or mRNA transcript-coding region as thecausative/associated SNP, other SNPs in the same exon or same intron asthe causative/associated SNP, other SNPs in the same haplotype block asthe causative/associated SNP, other SNPs in the same intergenic regionas the causative/associated SNP, SNPs that are outside but near a gene(e.g., within 6 kb on either side, 5′ or 3′, of a gene boundary) thatharbors a causative/associated SNP, etc. Such useful LD SNPs can beselected from among the SNPs disclosed in Tables 1-3, for example.

Linkage disequilibrium in the human genome is reviewed in the followingreferences: Wall et al. et al., “Haplotype blocks and linkagedisequilibrium in the human genome,”, Nat Rev Genet. 2003 August;4(8):587-97 (August 2003); Garner et al. et al., “On selecting markersfor association studies: patterns of linkage disequilibrium between twoand three diallelic loci,”, Genet Epidemiol. 2003 January;24(1):57-67(January 2003); Ardlie et al. et al., “Patterns of linkagedisequilibrium in the human genome,”, Nat Rev Genet. 2002 April;3(4):299-309 (April 2002); (erratum in Nat Rev Genet 2002 July; 3(7):566(July 2002); and Remm et al. et al., “High-density genotyping andlinkage disequilibrium in the human genome using chromosome 22 as amodel,”; Curr Opin Chem Biol. 2002 February; 6(1):24-30 (February 2002);J. B. S. Haldane, “JBS (1919) The combination of linkage values, and thecalculation of distances between the loci of linked factors,”. J Genet8:299-309 (1919); G. Mendel, G. (1866) Versuche über Pflanzen-Hybriden.Verhandlungen des naturforschenden Vereines in Brünn [(Proceedings ofthe Natural History Society of Brünn)] (1866); Lewin B (1990) Genes IV,B. Lewin, ed., Oxford University Press, New York N.Y., USA (1990); D. L.Hartl D L and A. G. Clark A G (1989) Principles of Population Genetics2^(nd) ed., Sinauer Associates, Inc., Ma Sunderland, Mass., USA (1989);J. H. Gillespie J H (2004) Population Genetics: A Concise Guide. 2^(nd)ed., Johns Hopkins University Press. (2004) USA; R. C. Lewontin, “RC(1964) The interaction of selection and linkage. I. Generalconsiderations; heterotic models,”. Genetics 49:49-67 (1964); P. G.Hoel, P G (1954) Introduction to Mathematical Statistics 2^(nd) ed.,John Wiley & Sons, Inc., N.Y. New York, USA (1954); R. R. Hudson, R R“(2001) Two-locus sampling distributions and their application,”.Genetics 159:1805-1817 (2001); A. P. Dempster A P, N. M. Laird, D. B. NM, Rubin, “DB (1977) Maximum likelihood from incomplete data via the EMalgorithm,”. J R Stat Soc 39:1-38 (1977); L. Excoffier L, M. Slatkin, M“(1995) Maximum-likelihood estimation of molecular haplotype frequenciesin a diploid population,”. Mol Biol Evol 12(5):921-927 (1995); D. A.Tregouet D A, S. Escolano S, L. Tiret L, A. Mallet A, J. L. Golmard, JL“(2004) A new algorithm for haplotype-based association analysis: theStochastic-EM algorithm,”. Ann Hum Genet 68(Pt 2):165-177 (2004); A. D.Long A D and C. H. Langley C H, “(1999) The power of association studiesto detect the contribution of candidate genetic loci to variation incomplex traits,”. Genome Research 9:720-731 (1999); A. Agresti, A (1990)Categorical Data Analysis, John Wiley & Sons, Inc., N.Y. New York, USA(1990); K. Lange, K (1997) Mathematical and Statistical Methods forGenetic Analysis, Springer-Verlag New York, Inc., N.Y. New York, USA(1997); The International HapMap Consortium, “(2003) The InternationalHapMap Project,”. Nature 426:789-796 (2003); The International HapMapConsortium, “(2005) A haplotype map of the human genome,”. Nature437:1299-1320 (2005); G. A. Thorisson G A, A. V. Smith A V, L. KrishnanL, L. D. Stein L D (2005), “The International HapMap Project Web Site,”.Genome Research 15:1591-1593 (2005); G. McVean, C. C. A. G, Spencer C CA, R. Chaix R (2005), “Perspectives on human genetic variation from theHapMap project,”. PLoS Genetics 1(4):413-418 (2005); J. N. Hirschhorn JN, M. J. Daly, MJ “(2005) Genome-wide association studies for commondiseases and complex traits,”. Nat Genet 6:95-108 (2005); S. J. Schrodi,“SJ (2005) A probabilistic approach to large-scale association scans: asemi-Bayesian method to detect disease-predisposing alleles,”. SAGMB4(1):31 (2005); W. Y. S. Wang W Y S, B. J. Barratt B J, D. G. Clayton DG, J. A. Todd, “JA (2005) Genome-wide association studies: theoreticaland practical concerns,”. Nat Rev Genet 6:109-118 (2005); J. K.Pritchard J K, M. Przeworski, “M (2001) Linkage disequilibrium inhumans: models and data,”. Am J Hum Genet 69:1-14 (2001).

As discussed above, one aspect of the present invention is the discoverythat SNPs which are in certain LD distance with the interrogated SNP canalso be used as valid markers for identifying an increased or decreasedrisks of having or developing psoriasis. As used herein, the term“interrogated SNP” refers to SNPs that have been found to be associatedwith an increased or decreased risk of disease using genotyping resultsand analysis, or other appropriate experimental method as exemplified inthe working examples described in this application. As used herein, theterm “LD SNP” refers to a SNP that has been characterized as a SNPassociating with an increased or decreased risk of diseases due to theirbeing in LD with the “interrogated SNP” under the methods of calculationdescribed in the application. Below, applicants describe the methods ofcalculation with which one of ordinary skilled in the art may determineif a particular SNP is in LD with an interrogated SNP. The parameter r²is commonly used in the genetics art to characterize the extent oflinkage disequilibrium between markers (Hudson, 2001). As used herein,the term “in LD with” refers to a particular SNP that is measured atabove the threshold of a parameter such as r² with an interrogated SNP.

It is now common place to directly observe genetic variants in a sampleof chromosomes obtained from a population. Suppose one has genotype dataat two genetic markers located on the same chromosome, for the markers Aand B. Further suppose that two alleles segregate at each of these twomarkers such that alleles A₁ and A₂ can be found at marker A and allelesB₁ and B₂ at marker B. Also assume that these two markers are on a humanautosome. If one is to examine a specific individual and find that theyare heterozygous at both markers, such that their two-marker genotype isA₁A₂B₁B₂, then there are two possible configurations: the individual inquestion could have the alleles A₁B₁ on one chromosome and A₂B₂ on theremaining chromosome; alternatively, the individual could have allelesA₁B₂ on one chromosome and A₂B₁ on the other. The arrangement of alleleson a chromosome is called a haplotype. In this illustration, theindividual could have haplotypes A₁B₁/A₂B₂ or A₁B₂/A₂B₁ (see Hartl andClark (1989) for a more complete description). The concept of linkageequilibrium relates the frequency of haplotypes to the allelefrequencies.

Assume that a sample of individuals is selected from a largerpopulation. Considering the two markers described above, each having twoalleles, there are four possible haplotypes: A₁B₁, A₁B₂, A₂B₁ and A₂B₂.Denote the frequencies of these four haplotypes with the followingnotation.

P ₁₁=freq(A ₁ B ₁)  (1)

P ₁₂=freq(A ₁ B ₂)  (2)

P ₂₁=freq(A ₂ B ₁)  (3)

P ₂₂=freq(A ₂ B ₂)  (4)

The allele frequencies at the two markers are then the sum of differenthaplotype frequencies, it is straightforward to write down a similar setof equations relating single-marker allele frequencies to two-markerhaplotype frequencies:

p ₁=freq(A ₁)=P ₁₁ +P ₁₂  (5)

p ₂=freq(A ₂)=P ₂₁ +P ₂₂  (6)

q ₁=freq(B ₁)=P ₁₁ +P ₂₁  (7)

q ₂=freq(B ₂)=P ₁₂ +P ₂₂  (8)

Note that the four haplotype frequencies and the allele frequencies ateach marker must sum to a frequency of 1.

P ₁₁ +P ₁₂ +P ₂₁ +P ₂₂=1  (9)

P ₁ +P ₂=1  (10)

q ₁ +q ₂=1  (11)

If there is no correlation between the alleles at the two markers, onewould expect that the frequency of the haplotypes would be approximatelythe product of the composite alleles. Therefore,

P ₁₁ ≈p ₁ q ₁  (12)

P ₁₂ ≈p ₁ q ₂  (13)

P ₂₁ ≈p ₂ q ₁  (14)

P ₂₂ ≈p ₂ q ₂  (15)

These approximating equations (12)-(15) represent the concept of linkageequilibrium where there is independent assortment between the twomarkers—the alleles at the two markers occur together at random. Theseare represented as approximations because linkage equilibrium andlinkage disequilibrium are concepts typically thought of as propertiesof a sample of chromosomes; and as such they are susceptible tostochastic fluctuations due to the sampling process. Empirically, manypairs of genetic markers will be in linkage equilibrium, but certainlynot all pairs.

Having established the concept of linkage equilibrium above, applicantscan now describe the concept of linkage disequilibrium (LD), which isthe deviation from linkage equilibrium. Since the frequency of the A₁ B₁haplotype is approximately the product of the allele frequencies for A₁and B₁ under the assumption of linkage equilibrium as statedmathematically in (12), a simple measure for the amount of departurefrom linkage equilibrium is the difference in these two quantities, D,

D=P ₁₁ −p ₁ q ₁  (16)

D=0 indicates perfect linkage equilibrium. Substantial departures fromD=0 indicates LD in the sample of chromosomes examined. Many propertiesof D are discussed in Lewontin (1964) including the maximum and minimumvalues that D can take. Mathematically, using basic algebra, it can beshown that D can also be written solely in terms of haplotypes:

D=P ₁₁ P ₂₂ −P ₁₂ P ₂₁  (17)

If one transforms D by squaring it and subsequently dividing by theproduct of the allele frequencies of A₁, A₂, B₁ and B₂, the resultingquantity, called r², is equivalent to the square of the Pearson'scorrelation coefficient commonly used in statistics (e.g. Hoel, 1954).

$\begin{matrix}{r^{2} = \frac{D^{2}}{p_{1}p_{2}q_{1}q_{2}}} & (18)\end{matrix}$

As with D, values of r² close to 0 indicate linkage equilibrium betweenthe two markers examined in the sample set. As values of r² increase,the two markers are said to be in linkage disequilibrium. The range ofvalues that r² can take are from 0 to 1. r²=1 when there is a perfectcorrelation between the alleles at the two markers.

In addition, the quantities discussed above are sample-specific. And assuch, it is necessary to formulate notation specific to the samplesstudied. In the approach discussed here, three types of samples are ofprimary interest: (i) a sample of chromosomes from individuals affectedby a disease-related phenotype (cases), (ii) a sample of chromosomesobtained from individuals not affected by the disease-related phenotype(controls), and (iii) a standard sample set used for the construction ofhaplotypes and calculation pairwise linkage disequilibrium. For theallele frequencies used in the development of the method describedbelow, an additional subscript will be added to denote either the caseor control sample sets.

P _(1,cs)=freq(A ₁ in cases)  (19)

P _(2,cs)=freq(A ₂ in cases)  (20)

q _(1,cs)=freq(B ₁ in cases)  (21)

q _(2,cs)=freq(B ₂ in cases)  (22)

Similarly,

P _(1,ct)=freq(A ₁ in controls)  (23)

P _(2,ct)=freq(A ₂ in controls)  (24)

q _(1,ct)=freq(B ₁ in controls)  (25)

q _(2,ct)=freq(B ₂ in controls)  (26)

As a well-accepted sample set is necessary for robust linkagedisequilibrium calculations, data obtained from the International HapMapproject (The International HapMap Consortium 2003, 2005; Thorisson etal, 2005; McVean et al, 2005) can be used for the calculation ofpairwise r² values. Indeed, the samples genotyped for the InternationalHapMap Project were selected to be representative examples from varioushuman sub-populations with sufficient numbers of chromosomes examined todraw meaningful and robust conclusions from the patterns of geneticvariation observed. The International HapMap project website(hapmap.org) contains a description of the project, methods utilized andsamples examined. It is useful to examine empirical data to get a senseof the patterns present in such data.

Haplotype frequencies were explicit arguments in equation (18) above.However, knowing the 2-marker haplotype frequencies requires that phaseto be determined for doubly heterozygous samples. When phase is unknownin the data examined, various algorithms can be used to infer phase fromthe genotype data. This issue was discussed earlier where the doublyheterozygous individual with a 2-SNP genotype of A₁A₂B₁B₂ could have oneof two different sets of chromosomes: A₁/B₁/A₂B₂ or A₁B₂/A₂B₁. One suchalgorithm to estimate haplotype frequencies is theexpectation-maximization (EM) algorithm first formalized by Dempster etal et al. (1977). This algorithm is often used in genetics to inferhaplotype frequencies from genotype data (e.g. e.g. Excoffier andSlatkin, (1995); Tregouet et al et al., (2004)). It should be noted thatfor the two-SNP case explored here, EM algorithms have very little errorprovided that the allele frequencies and sample sizes are not too small.The impact on r² values is typically negligible.

As correlated genetic markers share information, interrogation of SNPmarkers in LD with a disease-associated SNP marker can also havesufficient power to detect disease association (Long and Langley,(1999)). The relationship between the power to directly finddisease-associated alleles and the power to indirectly detectdisease-association was investigated by Pritchard and Przeworski (2001).In a straight-forward derivation, it can be shown that the power todetect disease association indirectly at a marker locus in linkagedisequilibrium with a disease-association locus is approximately thesame as the power to detect disease-association directly at thedisease-association locus if the sample size is increased by a factor of

$\frac{1}{r^{2}}$

(the reciprocal of equation 18) at the marker in comparison with thedisease-association locus.

Therefore, if one calculated the power to detect disease-associationindirectly with an experiment having N samples, then equivalent power todirectly detect disease-association (at the actualdisease-susceptibility locus) would necessitate an experiment usingapproximately r² N samples. This elementary relationship between power,sample size and linkage disequilibrium can be used to derive an r²threshold value useful in determining whether or not genotyping markersin linkage disequilibrium with a SNP marker directly associated withdisease status has enough power to indirectly detectdisease-association.

To commence a derivation of the power to detect disease-associatedmarkers through an indirect process, define the effective chromosomalsample size as

$\begin{matrix}{{n = \frac{4N_{cs}N_{ct}}{N_{cs} + N_{ct}}};} & (27)\end{matrix}$

where N_(cs) and N_(ct) are the numbers of diploid cases and controls,respectively. This is necessary to handle situations where the numbersof cases and controls are not equivalent. For equal case and controlsample sizes, N_(cs)=N_(ct)=N, the value of the effective number ofchromosomes is simply n=2N—as expected. Let power be calculated for asignificance level α (such that traditional P-values below α will bedeemed statistically significant). Define the standard Gaussiandistribution function as Φ(•). Mathematically,

$\begin{matrix}{{\Phi (x)} = {\frac{1}{\sqrt{2\pi}}{\int_{- \infty}^{x}{^{- \frac{\theta^{2}}{2}}{\theta}}}}} & (28)\end{matrix}$

Alternatively, the following error function notation (Erf) may also beused,

$\begin{matrix}{{\Phi (x)} = {\frac{1}{2}\left\lbrack {1 + {{Erf}\left( \frac{x}{\sqrt{2}} \right)}} \right\rbrack}} & (29)\end{matrix}$

For example, Φ(1.644854)=0.95. The value of r² may be derived to yield apre-specified minimum amount of power to detect disease associationthough indirect interrogation. Noting that the LD SNP marker could bethe one that is carrying the disease-association allele, therefore thatthis approach constitutes a lower-bound model where all indirect powerresults are expected to be at least as large as those interrogated.

Denote by β the error rate for not detecting truly disease-associatedmarkers. Therefore, 1−β is the classical definition of statisticalpower. Substituting the Pritchard-Pzreworski result into the samplesize, the power to detect disease association at a significance level ofα is given by the approximation

$\begin{matrix}{{{1 - \beta} \cong {\Phi\left\lbrack {\frac{{q_{1,{es}} - q_{1,{ct}}}}{\sqrt{\frac{{q_{1,{cs}}\left( {1 - q_{1,{cs}}} \right)} + {q_{1,{ct}}\left( {1 - q_{1,{ct}}} \right)}}{r^{2}n}}} - Z_{1 - {\alpha/2}}} \right\rbrack}};} & (30)\end{matrix}$

where Z_(u) is the inverse of the standard normal cumulativedistribution evaluated at u (uε(0,1)). Z_(u)=Φ⁻¹(u), whereΦ(Φ⁻¹(u))=Φ⁻¹(Φ(u))=u. For example, setting α=0.05, and therefore1−α/2=0.975, Z_(0.975)=1.95996 is obtained. Next, setting power equal toa threshold of a minimum power of T,

$\begin{matrix}{T = {\Phi\left\lbrack {\frac{{q_{1,{cs}} - q_{1,{ct}}}}{\sqrt{\frac{{q_{1,{cs}}\left( {1 - q_{1,{cs}}} \right)} + {q_{1,{ct}}\left( {1 - q_{1,{ct}}} \right)}}{r^{2}n}}} - Z_{1 - {\alpha/2}}} \right\rbrack}} & (31)\end{matrix}$

and solving for r², the following threshold r² is obtained:

$\begin{matrix}{{r_{T}^{2} = {\frac{\left\lbrack {{q_{1,{cs}}\left( {1 - q_{1,{cs}}} \right)} + {q_{1,{ct}}\left( {1 - q_{1,{ct}}} \right)}} \right\rbrack}{{n\left( {q_{1,{cs}} - q_{1,{ct}}} \right)}^{2}}\left\lbrack {{\Phi^{- 1}(T)} + Z_{1 - {\alpha/2}}} \right\rbrack}^{2}}{{Or},}} & (32) \\{r_{T}^{2} = {\frac{\left( {Z_{T} + Z_{1 - {\alpha/2}}} \right)^{2}}{n}\left\lbrack \frac{q_{1,{es}} - \left( q_{1,{cs}} \right)^{2} + q_{1,{ct}} - \left( q_{1,{ct}} \right)^{2}}{\left( {q_{1,{cs}} - q_{1,{ct}}} \right)^{2}} \right\rbrack}} & (33)\end{matrix}$

Suppose that r² is calculated between an interrogated SNP and a numberof other SNPs with varying levels of LD with the interrogated SNP. Thethreshold value r_(T) ² is the minimum value of linkage disequilibriumbetween the interrogated SNP and the potential LD SNPs such that the LDSNP still retains a power greater or equal to T for detectingdisease-association. For example, suppose that SNP rs200 is genotyped ina case-control disease-association study and it is found to beassociated with a disease phenotype. Further suppose that the minorallele frequency in 1,000 case chromosomes was found to be 16% incontrast with a minor allele frequency of 10% in 1,000 controlchromosomes. Given those measurements one could have predicted, prior tothe experiment, that the power to detect disease association at asignificance level of 0.05 was quite high—approximately 98% using a testof allelic association. Applying equation (32) one can calculate aminimum value of r² to indirectly assess disease association assumingthat the minor allele at SNP rs200 is truly disease-predisposing for athreshold level of power. If one sets the threshold level of power to be80%, then r_(T) ²=0.489 given the same significance level and chromosomenumbers as above. Hence, any SNP with a pairwise r² value with rs200greater than 0.489 is expected to have greater than 80% power to detectthe disease association. Further, this is assuming the conservativemodel where the LD SNP is disease-associated only through linkagedisequilibrium with the interrogated SNP rs200.

The contribution or association of particular SNPs and/or SNP haplotypeswith disease phenotypes, such as psoriasis, enables the SNPs of thepresent invention to be used to develop superior diagnostic testscapable of identifying individuals who express a detectable trait, suchas psoriasis, as the result of a specific genotype, or individuals whosegenotype places them at an increased or decreased risk of developing adetectable trait at a subsequent time as compared to individuals who donot have that genotype. As described herein, diagnostics may be based ona single SNP or a group of SNPs. Combined detection of a plurality ofSNPs (for example, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, 24, 25, 30, 32, 48, 50, 64, 96, 100, or any other numberin-between, or more, of the SNPs provided in Table 1 and/or Table 2)typically increases the probability of an accurate diagnosis. Forexample, the presence of a single SNP known to correlate with psoriasismight indicate a probability of 20% that an individual has or is at riskof developing psoriasis, whereas detection of five SNPs, each of whichcorrelates with psoriasis, might indicate a probability of 80% that anindividual has or is at risk of developing psoriasis. To furtherincrease the accuracy of diagnosis or predisposition screening, analysisof the SNPs of the present invention can be combined with that of otherpolymorphisms or other risk factors of psoriasis, such as diseasesymptoms, pathological characteristics, family history, diet,environmental factors or lifestyle factors.

It will, of course, be understood by practitioners skilled in thetreatment or diagnosis of psoriasis that the present invention generallydoes not intend to provide an absolute identification of individuals whoare at risk (or less at risk) of developing psoriasis, and/orpathologies related to psoriasis, but rather to indicate a certainincreased (or decreased) degree or likelihood of developing the diseasebased on statistically significant association results. However, thisinformation is extremely valuable as it can be used to, for example,initiate preventive treatments or to allow an individual carrying one ormore significant SNPs or SNP haplotypes to foresee warning signs such asminor clinical symptoms, or to have regularly scheduled physical examsto monitor for appearance of a condition in order to identify and begintreatment of the condition at an early stage. Particularly with diseasesthat are extremely debilitating or fatal if not treated on time, theknowledge of a potential predisposition, even if this predisposition isnot absolute, would likely contribute in a very significant manner totreatment efficacy.

The diagnostic techniques of the present invention may employ a varietyof methodologies to determine whether a test subject has a SNP or a SNPpattern associated with an increased or decreased risk of developing adetectable trait or whether the individual suffers from a detectabletrait as a result of a particular polymorphism/mutation, including, forexample, methods which enable the analysis of individual chromosomes forhaplotyping, family studies, single sperm DNA analysis, or somatichybrids. The trait analyzed using the diagnostics of the invention maybe any detectable trait that is commonly observed in pathologies anddisorders related to psoriasis.

Another aspect of the present invention relates to a method ofdetermining whether an individual is at risk (or less at risk) ofdeveloping one or more traits or whether an individual expresses one ormore traits as a consequence of possessing a particular trait-causing ortrait-influencing allele. These methods generally involve obtaining anucleic acid sample from an individual and assaying the nucleic acidsample to determine which nucleotide(s) is/are present at one or moreSNP positions, wherein the assayed nucleotide(s) is/are indicative of anincreased or decreased risk of developing the trait or indicative thatthe individual expresses the trait as a result of possessing aparticular trait-causing or trait-influencing allele.

In another embodiment, the SNP detection reagents of the presentinvention are used to determine whether an individual has one or moreSNP allele(s) affecting the level (e.g., the concentration of mRNA orprotein in a sample, etc.) or pattern (e.g., the kinetics of expression,rate of decomposition, stability profile, Km, Vmax, etc.) of geneexpression (collectively, the “gene response” of a cell or bodilyfluid). Such a determination can be accomplished by screening for mRNAor protein expression (e.g., by using nucleic acid arrays, RT-PCR,TaqMan assays, or mass spectrometry), identifying genes having alteredexpression in an individual, genotyping SNPs disclosed in Table 1 and/orTable 2 that could affect the expression of the genes having alteredexpression (e.g., SNPs that are in and/or around the gene(s) havingaltered expression, SNPs in regulatory/control regions, SNPs in and/oraround other genes that are involved in pathways that could affect theexpression of the gene(s) having altered expression, or all SNPs couldbe genotyped), and correlating SNP genotypes with altered geneexpression. In this manner, specific SNP alleles at particular SNP sitescan be identified that affect gene expression.

Pharmacogenomics and Therapeutics/Drug Development

The present invention provides methods for assessing thepharmacogenomics of a subject harboring particular SNP alleles orhaplotypes or diplotypes to a particular therapeutic agent orpharmaceutical compound, or to a class of such compounds.Pharmacogenomics deals with the roles which clinically significanthereditary variations (e.g., SNPs) play in the response to drugs due toaltered drug disposition and/or abnormal action in affected persons.See, e.g., Roses, Nature 405, 857-865 (2000); Gould Rothberg, NatureBiotechnology 19, 209-211 (2001); Eichelbaum, Clin. Exp. Pharmacol.Physiol. 23(10-11):983-985 (1996); and Linder, Clin. Chem. 43(2):254-266(1997). The clinical outcomes of these variations can result in severetoxicity of therapeutic drugs in certain individuals or therapeuticfailure of drugs in certain individuals as a result of individualvariation in metabolism. Thus, the SNP genotype of an individual candetermine the way a therapeutic compound acts on the body or the way thebody metabolizes the compound. For example, SNPs in drug metabolizingenzymes can affect the activity of these enzymes, which in turn canaffect both the intensity and duration of drug action, as well as drugmetabolism and clearance.

The discovery of SNPs in drug metabolizing enzymes, drug transporters,proteins for pharmaceutical agents, and other drug targets has explainedwhy some patients do not obtain the expected drug effects, show anexaggerated drug effect, or experience serious toxicity from standarddrug dosages. SNPs can be expressed in the phenotype of the extensivemetabolizer and in the phenotype of the poor metabolizer. Accordingly,SNPs may lead to allelic variants of a protein in which one or more ofthe protein functions in one population are different from those inanother population. SNPs and the encoded variant peptides thus providetargets to ascertain a genetic predisposition that can affect treatmentmodality. For example, in a ligand-based treatment, SNPs may give riseto amino terminal extracellular domains and/or other ligand-bindingregions of a receptor that are more or less active in ligand binding,thereby affecting subsequent protein activation. Accordingly, liganddosage would necessarily be modified to maximize the therapeutic effectwithin a given population containing particular SNP alleles orhaplotypes.

As an alternative to genotyping, specific variant proteins containingvariant amino acid sequences encoded by alternative SNP alleles could beidentified. Thus, pharmacogenomic characterization of an individualpermits the selection of effective compounds and effective dosages ofsuch compounds for prophylactic or therapeutic uses based on theindividual's SNP genotype, thereby enhancing and optimizing theeffectiveness of the therapy. Furthermore, the production of recombinantcells and transgenic animals containing particular SNPs/haplotypes alloweffective clinical design and testing of treatment compounds and dosageregimens. For example, transgenic animals can be produced that differonly in specific SNP alleles in a gene that is orthologous to a humandisease susceptibility gene.

Pharmacogenomic uses of the SNPs of the present invention provideseveral significant advantages for patient care, particularly intreating psoriasis. Pharmacogenomic characterization of an individual,based on an individual's SNP genotype, can identify those individualsunlikely to respond to treatment with a particular medication andthereby allows physicians to avoid prescribing the ineffectivemedication to those individuals. On the other hand, SNP genotyping of anindividual may enable physicians to select the appropriate medicationand dosage regimen that will be most effective based on an individual'sSNP genotype. This information increases a physician's confidence inprescribing medications and motivates patients to comply with their drugregimens. Furthermore, pharmacogenomics may identify patientspredisposed to toxicity and adverse reactions to particular drugs ordrug dosages. Adverse drug reactions lead to more than 100,000 avoidabledeaths per year in the United States alone and therefore represent asignificant cause of hospitalization and death, as well as a significanteconomic burden on the healthcare system (Pfost et. al., Trends inBiotechnology, August 2000.). Thus, pharmacogenomics based on the SNPsdisclosed herein has the potential to both save lives and reducehealthcare costs substantially.

Attempts have been made to develop drugs that can be used to treatpsoriasis. Several drug candidates have been introduced into clinicaltrials to test their efficacy in treating psoriasis. Among them areABT-874, STA-5326, and CNTO-1275. For references discussing these drugs,please see Burakoff, et al., A phase 1/2A trial of STA 5326, an oralinterleukin-12/23 inhibitor, in patients with active moderate to severeCrohn's disease. Inflamm Bowel Dis. 2006 July; 12(7):558-65, andBorchardt J K, Focus on small molecule inhibitors for treatment ofinflammatory and autoimmune diseases. Drug News Perspect. 2004 November;17(9):607-14 (for STA-5326); Sandborn W J, How future tumor necrosisfactor antagonists and other compounds will meet the remainingchallenges in Crohn's disease. Rev Gastroenterol Disord. 2004; 4 Suppl3:S25-33 (for ABT-874); Papp K A. Potential future therapies forpsoriasis. Semin Cutan Med Surg. 2005 March; 24(1):58-63 (forCNTO-1275). These drug candidates target the metabolic pathway involvingIL-12 and IL-23 genes.

It is also well known in the art that markers that are diagnosticallyuseful in distinguishing patients at higher risk of developing adisease, such as psoriasis from those who are at a decreased risk ofdeveloping psoriasis can be useful in identifying those patients thatare more likely to respond to drug treatments targeting at thosepathways involving genes where the diagnostic SNPs reside. Seereferences Gerdes, et al., Circulation, 2000; 101:1366-1371,Kuivenhoven, et al., N Engl J Med 1998; 338:86-93, Stolarz, et al.,Hypertension 2004; 44:156-162, Chartier-Harlin, et al., Hum. Mol. Genet.1994 April; 3(4):569-74, Roses, et al., The Pharmacogenomics Journal2006, 1-19.

In that regard, embodiments of the present invention can be very usefulin assisting clinicians select patients who are more likely to developpsoriasis, and are in turn good candidates for drug responses targetingpsoriasis, thus warrant the application of the above-mentioned drugtreatments on such patients. In the mean time, patients who are deemedto have low risk of developing psoriasis, using SNP markers discoveredherein, can be spared of the aggravation and wastefulness of the drugdue to the reduced benefit of such treatment in view of its cost andside effect.

Pharmacogenomics in general is discussed further in Rose et al.,“Pharmacogenetic analysis of clinically relevant genetic polymorphisms”,Methods Mol Med. 2003; 85:225-37. Pharmacogenomics as it relates toAlzheimer's disease and other neurodegenerative disorders is discussedin Cacabelos, “Pharmacogenomics for the treatment of dementia”, Ann Med.2002; 34(5):357-79, Maimone et al., “Pharmacogenomics ofneurodegenerative diseases”, Eur J Pharmacol. 2001 Feb. 9; 413(1):11-29,and Poirier, “Apolipoprotein E: a pharmacogenetic target for thetreatment of Alzheimer's disease”, Mol Diagn. 1999 December;4(4):335-41. Pharmacogenomics as it relates to cardiovascular disordersis discussed in Siest et al., “Pharmacogenomics of drugs affecting thecardiovascular system”, Clin Chem Lab Med. 2003 April; 41(4):590-9,Mukherjee et al., “Pharmacogenomics in cardiovascular diseases”, ProgCardiovasc Dis. 2002 May-June; 44(6):479-98, and Mooser et al.,“Cardiovascular pharmacogenetics in the SNP era”, J Thromb Haemost. 2003July; 1(7):1398-402. Pharmacogenomics as it relates to cancer isdiscussed in McLeod et al., “Cancer pharmacogenomics: SNPs, chips, andthe individual patient”, Cancer Invest. 2003; 21(4):630-40 and Watterset al., “Cancer pharmacogenomics: current and future applications”,Biochim Biophys Acta. 2003 Mar. 17; 1603(2):99-111.

The SNPs of the present invention also can be used to identify noveltherapeutic targets for psoriasis. For example, genes containing thedisease-associated variants (“variant genes”) or their products, as wellas genes or their products that are directly or indirectly regulated byor interacting with these variant genes or their products, can betargeted for the development of therapeutics that, for example, treatthe disease or prevent or delay disease onset. The therapeutics may becomposed of, for example, small molecules, proteins, protein fragmentsor peptides, antibodies, nucleic acids, or their derivatives or mimeticswhich modulate the functions or levels of the target genes or geneproducts.

The SNP-containing nucleic acid molecules disclosed herein, and theircomplementary nucleic acid molecules, may be used as antisenseconstructs to control gene expression in cells, tissues, and organisms.Antisense technology is well established in the art and extensivelyreviewed in Antisense Drug Technology: Principles, Strategies, andApplications, Crooke (ed.), Marcel Dekker, Inc.: New York (2001). Anantisense nucleic acid molecule is generally designed to becomplementary to a region of mRNA expressed by a gene so that theantisense molecule hybridizes to the mRNA and thereby blocks translationof mRNA into protein. Various classes of antisense oligonucleotides areused in the art, two of which are cleavers and blockers. Cleavers, bybinding to target RNAs, activate intracellular nucleases (e.g., RNaseHor RNase L) that cleave the target RNA. Blockers, which also bind totarget RNAs, inhibit protein translation through steric hindrance ofribosomes. Exemplary blockers include peptide nucleic acids,morpholinos, locked nucleic acids, and methylphosphonates (see, e.g.,Thompson, Drug Discovery Today, 7 (17): 912-917 (2002)). Antisenseoligonucleotides are directly useful as therapeutic agents, and are alsouseful for determining and validating gene function (e.g., in geneknock-out or knock-down experiments).

Antisense technology is further reviewed in: Lavery et al., “Antisenseand RNAi: powerful tools in drug target discovery and validation”, CurrOpin Drug Discov Devel. 2003 July; 6(4):561-9; Stephens et al.,“Antisense oligonucleotide therapy in cancer”, Curr Opin Mol Ther. 2003April; 5(2):118-22; Kurreck, “Antisense technologies. Improvementthrough novel chemical modifications”, Eur J Biochem. 2003 April;270(8):1628-44; Dias et al., “Antisense oligonucleotides: basic conceptsand mechanisms”, Mol Cancer Ther. 2002 March; 1(5):347-55; Chen,“Clinical development of antisense oligonucleotides as anti-cancertherapeutics”, Methods Mol Med. 2003; 75:621-36; Wang et al., “Antisenseanticancer oligonucleotide therapeutics”, Curr Cancer Drug Targets. 2001November; 1(3):177-96; and Bennett, “Efficiency of antisenseoligonucleotide drug discovery”, Antisense Nucleic Acid Drug Dev. 2002June; 12(3):215-24.

The SNPs of the present invention are particularly useful for designingantisense reagents that are specific for particular nucleic acidvariants. Based on the SNP information disclosed herein, antisenseoligonucleotides can be produced that specifically target mRNA moleculesthat contain one or more particular SNP nucleotides. In this manner,expression of mRNA molecules that contain one or more undesiredpolymorphisms (e.g., SNP nucleotides that lead to a defective proteinsuch as an amino acid substitution in a catalytic domain) can beinhibited or completely blocked. Thus, antisense oligonucleotides can beused to specifically bind a particular polymorphic form (e.g., a SNPallele that encodes a defective protein), thereby inhibiting translationof this form, but which do not bind an alternative polymorphic form(e.g., an alternative SNP nucleotide that encodes a protein havingnormal function).

Antisense molecules can be used to inactivate mRNA in order to inhibitgene expression and production of defective proteins. Accordingly, thesemolecules can be used to treat a disorder, such as psoriasis,characterized by abnormal or undesired gene expression or expression ofcertain defective proteins. This technique can involve cleavage by meansof ribozymes containing nucleotide sequences complementary to one ormore regions in the mRNA that attenuate the ability of the mRNA to betranslated. Possible mRNA regions include, for example, protein-codingregions and particularly protein-coding regions corresponding tocatalytic activities, substrate/ligand binding, or other functionalactivities of a protein.

The SNPs of the present invention are also useful for designing RNAinterference reagents that specifically target nucleic acid moleculeshaving particular SNP variants. RNA interference (RNAi), also referredto as gene silencing, is based on using double-stranded RNA (dsRNA)molecules to turn genes off. When introduced into a cell, dsRNAs areprocessed by the cell into short fragments (generally about 21, 22, or23 nucleotides in length) known as small interfering RNAs (siRNAs) whichthe cell uses in a sequence-specific manner to recognize and destroycomplementary RNAs (Thompson, Drug Discovery Today, 7 (17): 912-917(2002)). Accordingly, an aspect of the present invention specificallycontemplates isolated nucleic acid molecules that are about 18-26nucleotides in length, preferably 19-25 nucleotides in length, and morepreferably 20, 21, 22, or 23 nucleotides in length, and the use of thesenucleic acid molecules for RNAi. Because RNAi molecules, includingsiRNAs, act in a sequence-specific manner, the SNPs of the presentinvention can be used to design RNAi reagents that recognize and destroynucleic acid molecules having specific SNP alleles/nucleotides (such asdeleterious alleles that lead to the production of defective proteins),while not affecting nucleic acid molecules having alternative SNPalleles (such as alleles that encode proteins having normal function).As with antisense reagents, RNAi reagents may be directly useful astherapeutic agents (e.g., for turning off defective, disease-causinggenes), and are also useful for characterizing and validating genefunction (e.g., in gene knock-out or knock-down experiments).

The following references provide a further review of RNAi: Reynolds etal., “Rational siRNA design for RNA interference”, Nat Biotechnol. 2004March; 22(3):326-30. Epub 2004 Feb. 1; Chi et al., “Genomewide view ofgene silencing by small interfering RNAs”, PNAS 100(11):6343-6346, 2003;Vickers et al., “Efficient Reduction of Target RNAs by Small InterferingRNA and RNase H-dependent Antisense Agents”, J. Biol. Chem. 278:7108-7118, 2003; Agami, “RNAi and related mechanisms and their potentialuse for therapy”, Curr Opin Chem. Biol. 2002 December; 6(6):829-34;Lavery et al., “Antisense and RNAi: powerful tools in drug targetdiscovery and validation”, Curr Opin Drug Discov Devel. 2003 July;6(4):561-9; Shi, “Mammalian RNAi for the masses”, Trends Genet 2003January;19(1):9-12), Shuey et al., “RNAi: gene-silencing in therapeuticintervention”, Drug Discovery Today 2002 October; 7(20):1040-1046;McManus et al., Nat Rev Genet 2002 October; 3(10):737-47; Xia et al.,Nat Biotechnol 2002 October; 20(10):1006-10; Plasterk et al., Curr OpinGenet Dev 2000 October; 10(5):562-7; Bosher et al., Nat Cell Biol 2000February; 2(2):E31-6; and Hunter, Curr Biol 1999 Jun. 17; 9(12):R440-2).

A subject suffering from a pathological condition, such as psoriasis,ascribed to a SNP may be treated so as to correct the genetic defect(see Kren et al., Proc. Natl. Acad. Sci. USA 96:10349-10354 (1999)).Such a subject can be identified by any method that can detect thepolymorphism in a biological sample drawn from the subject. Such agenetic defect may be permanently corrected by administering to such asubject a nucleic acid fragment incorporating a repair sequence thatsupplies the normal/wild-type nucleotide at the position of the SNP.This site-specific repair sequence can encompass an RNA/DNAoligonucleotide that operates to promote endogenous repair of asubject's genomic DNA. The site-specific repair sequence is administeredin an appropriate vehicle, such as a complex with polyethylenimine,encapsulated in anionic liposomes, a viral vector such as an adenovirus,or other pharmaceutical composition that promotes intracellular uptakeof the administered nucleic acid. A genetic defect leading to an inbornpathology may then be overcome, as the chimeric oligonucleotides induceincorporation of the normal sequence into the subject's genome. Uponincorporation, the normal gene product is expressed, and the replacementis propagated, thereby engendering a permanent repair and therapeuticenhancement of the clinical condition of the subject.

In cases in which a cSNP results in a variant protein that is ascribedto be the cause of, or a contributing factor to, a pathologicalcondition, a method of treating such a condition can includeadministering to a subject experiencing the pathology thewild-type/normal cognate of the variant protein. Once administered in aneffective dosing regimen, the wild-type cognate provides complementationor remediation of the pathological condition.

The invention further provides a method for identifying a compound oragent that can be used to treat psoriasis. The SNPs disclosed herein areuseful as targets for the identification and/or development oftherapeutic agents. A method for identifying a therapeutic agent orcompound typically includes assaying the ability of the agent orcompound to modulate the activity and/or expression of a SNP-containingnucleic acid or the encoded product and thus identifying an agent or acompound that can be used to treat a disorder characterized by undesiredactivity or expression of the SNP-containing nucleic acid or the encodedproduct. The assays can be performed in cell-based and cell-freesystems. Cell-based assays can include cells naturally expressing thenucleic acid molecules of interest or recombinant cells geneticallyengineered to express certain nucleic acid molecules.

Variant gene expression in a psoriasis patient can include, for example,either expression of a SNP-containing nucleic acid sequence (forinstance, a gene that contains a SNP can be transcribed into an mRNAtranscript molecule containing the SNP, which can in turn be translatedinto a variant protein) or altered expression of a normal/wild-typenucleic acid sequence due to one or more SNPs (for instance, aregulatory/control region can contain a SNP that affects the level orpattern of expression of a normal transcript).

Assays for variant gene expression can involve direct assays of nucleicacid levels (e.g., mRNA levels), expressed protein levels, or ofcollateral compounds involved in a signal pathway. Further, theexpression of genes that are up- or down-regulated in response to thesignal pathway can also be assayed. In this embodiment, the regulatoryregions of these genes can be operably linked to a reporter gene such asluciferase.

Modulators of variant gene expression can be identified in a methodwherein, for example, a cell is contacted with a candidatecompound/agent and the expression of mRNA determined. The level ofexpression of mRNA in the presence of the candidate compound is comparedto the level of expression of mRNA in the absence of the candidatecompound. The candidate compound can then be identified as a modulatorof variant gene expression based on this comparison and be used to treata disorder such as psoriasis that is characterized by variant geneexpression (e.g., either expression of a SNP-containing nucleic acid oraltered expression of a normal/wild-type nucleic acid molecule due toone or more SNPs that affect expression of the nucleic acid molecule)due to one or more SNPs of the present invention. When expression ofmRNA is statistically significantly greater in the presence of thecandidate compound than in its absence, the candidate compound isidentified as a stimulator of nucleic acid expression. When nucleic acidexpression is statistically significantly less in the presence of thecandidate compound than in its absence, the candidate compound isidentified as an inhibitor of nucleic acid expression.

The invention further provides methods of treatment, with the SNP orassociated nucleic acid domain (e.g., catalytic domain,ligand/substrate-binding domain, regulatory/control region, etc.) orgene, or the encoded mRNA transcript, as a target, using a compoundidentified through drug screening as a gene modulator to modulatevariant nucleic acid expression. Modulation can include eitherup-regulation (i.e., activation or agonization) or down-regulation(i.e., suppression or antagonization) of nucleic acid expression.

Expression of mRNA transcripts and encoded proteins, either wild type orvariant, may be altered in individuals with a particular SNP allele in aregulatory/control element, such as a promoter or transcription factorbinding domain, that regulates expression. In this situation, methods oftreatment and compounds can be identified, as discussed herein, thatregulate or overcome the variant regulatory/control element, therebygenerating normal, or healthy, expression levels of either the wild typeor variant protein.

The SNP-containing nucleic acid molecules of the present invention arealso useful for monitoring the effectiveness of modulating compounds onthe expression or activity of a variant gene, or encoded product, inclinical trials or in a treatment regimen. Thus, the gene expressionpattern can serve as an indicator for the continuing effectiveness oftreatment with the compound, particularly with compounds to which apatient can develop resistance, as well as an indicator for toxicities.The gene expression pattern can also serve as a marker indicative of aphysiological response of the affected cells to the compound.Accordingly, such monitoring would allow either increased administrationof the compound or the administration of alternative compounds to whichthe patient has not become resistant. Similarly, if the level of nucleicacid expression falls below a desirable level, administration of thecompound could be commensurately decreased.

In another aspect of the present invention, there is provided apharmaceutical pack comprising a therapeutic agent (e.g., a smallmolecule drug, antibody, peptide, antisense or RNAi nucleic acidmolecule, etc.) and a set of instructions for administration of thetherapeutic agent to humans diagnostically tested for one or more SNPsor SNP haplotypes provided by the present invention.

The SNPs/haplotypes of the present invention are also useful forimproving many different aspects of the drug development process. Forinstance, an aspect of the present invention includes selectingindividuals for clinical trials based on their SNP genotype. Forexample, individuals with SNP genotypes that indicate that they arelikely to positively respond to a drug can be included in the trials,whereas those individuals whose SNP genotypes indicate that they areless likely to or would not respond to the drug, or who are at risk forsuffering toxic effects or other adverse reactions, can be excluded fromthe clinical trials. This not only can improve the safety of clinicaltrials, but also can enhance the chances that the trial will demonstratestatistically significant efficacy. Furthermore, the SNPs of the presentinvention may explain why certain previously developed drugs performedpoorly in clinical trials and may help identify a subset of thepopulation that would benefit from a drug that had previously performedpoorly in clinical trials, thereby “rescuing” previously developeddrugs, and enabling the drug to be made available to a particularpsoriasis patient population that can benefit from it.

SNPs have many important uses in drug discovery, screening, anddevelopment. A high probability exists that, for any gene/proteinselected as a potential drug target, variants of that gene/protein willexist in a patient population. Thus, determining the impact ofgene/protein variants on the selection and delivery of a therapeuticagent should be an integral aspect of the drug discovery and developmentprocess. (Jazwinska, A Trends Guide to Genetic Variation and GenomicMedicine, 2002 March; S30-S36).

Knowledge of variants (e.g., SNPs and any corresponding amino acidpolymorphisms) of a particular therapeutic target (e.g., a gene, mRNAtranscript, or protein) enables parallel screening of the variants inorder to identify therapeutic candidates (e.g., small moleculecompounds, antibodies, antisense or RNAi nucleic acid compounds, etc.)that demonstrate efficacy across variants (Rothberg, Nat Biotechnol 2001March; 19(3):209-11). Such therapeutic candidates would be expected toshow equal efficacy across a larger segment of the patient population,thereby leading to a larger potential market for the therapeuticcandidate.

Furthermore, identifying variants of a potential therapeutic targetenables the most common form of the target to be used for selection oftherapeutic candidates, thereby helping to ensure that the experimentalactivity that is observed for the selected candidates reflects the realactivity expected in the largest proportion of a patient population(Jazwinska, A Trends Guide to Genetic Variation and Genomic Medicine,2002 March; S30-S36).

Additionally, screening therapeutic candidates against all knownvariants of a target can enable the early identification of potentialtoxicities and adverse reactions relating to particular variants. Forexample, variability in drug absorption, distribution, metabolism andexcretion (ADME) caused by, for example, SNPs in therapeutic targets ordrug metabolizing genes, can be identified, and this information can beutilized during the drug development process to minimize variability indrug disposition and develop therapeutic agents that are safer across awider range of a patient population. The SNPs of the present invention,including the variant proteins and encoding polymorphic nucleic acidmolecules provided in Tables 1-2, are useful in conjunction with avariety of toxicology methods established in the art, such as those setforth in Current Protocols in Toxicology, John Wiley & Sons, Inc., N.Y.

Furthermore, therapeutic agents that target any art-known proteins (ornucleic acid molecules, either RNA or DNA) may cross-react with thevariant proteins (or polymorphic nucleic acid molecules) disclosed inTable 1, thereby significantly affecting the pharmacokinetic propertiesof the drug. Consequently, the protein variants and the SNP-containingnucleic acid molecules disclosed in Tables 1-2 are useful in developing,screening, and evaluating therapeutic agents that target correspondingart-known protein forms (or nucleic acid molecules). Additionally, asdiscussed above, knowledge of all polymorphic forms of a particular drugtarget enables the design of therapeutic agents that are effectiveagainst most or all such polymorphic forms of the drug target.

Pharmaceutical Compositions and Administration Thereof

Any of the psoriasis-associated proteins, and encoding nucleic acidmolecules, disclosed herein can be used as therapeutic targets (ordirectly used themselves as therapeutic compounds) for treatingpsoriasis and related pathologies, and the present disclosure enablestherapeutic compounds (e.g., small molecules, antibodies, therapeuticproteins, RNAi and antisense molecules, etc.) to be developed thattarget (or are comprised of) any of these therapeutic targets.

In general, a therapeutic compound will be administered in atherapeutically effective amount by any of the accepted modes ofadministration for agents that serve similar utilities. The actualamount of the therapeutic compound of this invention, i.e., the activeingredient, will depend upon numerous factors such as the severity ofthe disease to be treated, the age and relative health of the subject,the potency of the compound used, the route and form of administration,and other factors.

Therapeutically effective amounts of therapeutic compounds may rangefrom, for example, approximately 0.01-50 mg per kilogram body weight ofthe recipient per day; preferably about 0.1-20 mg/kg/day. Thus, as anexample, for administration to a 70 kg person, the dosage range wouldmost preferably be about 7 mg to 1.4 g per day.

In general, therapeutic compounds will be administered as pharmaceuticalcompositions by any one of the following routes: oral, systemic (e.g.,transdermal, intranasal, or by suppository), or parenteral (e.g.,intramuscular, intravenous, or subcutaneous) administration. Thepreferred manner of administration is oral or parenteral using aconvenient daily dosage regimen, which can be adjusted according to thedegree of affliction. Oral compositions can take the form of tablets,pills, capsules, semisolids, powders, sustained release formulations,solutions, suspensions, elixirs, aerosols, or any other appropriatecompositions.

The choice of formulation depends on various factors such as the mode ofdrug administration (e.g., for oral administration, formulations in theform of tablets, pills, or capsules are preferred) and thebioavailability of the drug substance. Recently, pharmaceuticalformulations have been developed especially for drugs that show poorbioavailability based upon the principle that bioavailability can beincreased by increasing the surface area, i.e., decreasing particlesize. For example, U.S. Pat. No. 4,107,288 describes a pharmaceuticalformulation having particles in the size range from 10 to 1,000 nm inwhich the active material is supported on a cross-linked matrix ofmacromolecules. U.S. Pat. No. 5,145,684 describes the production of apharmaceutical formulation in which the drug substance is pulverized tonanoparticles (average particle size of 400 nm) in the presence of asurface modifier and then dispersed in a liquid medium to give apharmaceutical formulation that exhibits remarkably highbioavailability.

Pharmaceutical compositions are comprised of, in general, a therapeuticcompound in combination with at least one pharmaceutically acceptableexcipient. Acceptable excipients are non-toxic, aid administration, anddo not adversely affect the therapeutic benefit of the therapeuticcompound. Such excipients may be any solid, liquid, semi-solid or, inthe case of an aerosol composition, gaseous excipient that is generallyavailable to one skilled in the art.

Solid pharmaceutical excipients include starch, cellulose, talc,glucose, lactose, sucrose, gelatin, malt, rice, flour, chalk, silicagel, magnesium stearate, sodium stearate, glycerol monostearate, sodiumchloride, dried skim milk and the like. Liquid and semisolid excipientsmay be selected from glycerol, propylene glycol, water, ethanol andvarious oils, including those of petroleum, animal, vegetable orsynthetic origin, e.g., peanut oil, soybean oil, mineral oil, sesameoil, etc. Preferred liquid carriers, particularly for injectablesolutions, include water, saline, aqueous dextrose, and glycols.

Compressed gases may be used to disperse a compound of this invention inaerosol form. Inert gases suitable for this purpose are nitrogen, carbondioxide, etc.

Other suitable pharmaceutical excipients and their formulations aredescribed in Remington's Pharmaceutical Sciences, edited by E. W. Martin(Mack Publishing Company, 18^(th) ed., 1990).

The amount of the therapeutic compound in a formulation can vary withinthe full range employed by those skilled in the art. Typically, theformulation will contain, on a weight percent (wt %) basis, from about0.01-99.99 wt % of the therapeutic compound based on the totalformulation, with the balance being one or more suitable pharmaceuticalexcipients. Preferably, the compound is present at a level of about 1-80wt %.

Therapeutic compounds can be administered alone or in combination withother therapeutic compounds or in combination with one or more otheractive ingredient(s). For example, an inhibitor or stimulator of apsoriasis-associated protein can be administered in combination withanother agent that inhibits or stimulates the activity of the same or adifferent psoriasis-associated protein to thereby counteract the affectsof psoriasis.

For further information regarding pharmacology, see Current Protocols inPharmacology, John Wiley & Sons, Inc., N.Y.

Human Identification Applications

In addition to their diagnostic and therapeutic uses in psoriasis andrelated pathologies, the SNPs provided by the present invention are alsouseful as human identification markers for such applications asforensics, paternity testing, and biometrics (see, e.g., Gill, “Anassessment of the utility of single nucleotide polymorphisms (SNPs) forforensic purposes”, Int J Legal Med. 2001; 114(4-5):204-10). Geneticvariations in the nucleic acid sequences between individuals can be usedas genetic markers to identify individuals and to associate a biologicalsample with an individual. Determination of which nucleotides occupy aset of SNP positions in an individual identifies a set of SNP markersthat distinguishes the individual. The more SNP positions that areanalyzed, the lower the probability that the set of SNPs in oneindividual is the same as that in an unrelated individual. Preferably,if multiple sites are analyzed, the sites are unlinked (i.e., inheritedindependently). Thus, preferred sets of SNPs can be selected from amongthe SNPs disclosed herein, which may include SNPs on differentchromosomes, SNPs on different chromosome arms, and/or SNPs that aredispersed over substantial distances along the same chromosome arm.

Furthermore, among the SNPs disclosed herein, preferred SNPs for use incertain forensic/human identification applications include SNPs locatedat degenerate codon positions (i.e., the third position in certaincodons which can be one of two or more alternative nucleotides and stillencode the same amino acid), since these SNPs do not affect the encodedprotein. SNPs that do not affect the encoded protein are expected to beunder less selective pressure and are therefore expected to be morepolymorphic in a population, which is typically an advantage forforensic/human identification applications. However, for certainforensics/human identification applications, such as predictingphenotypic characteristics (e.g., inferring ancestry or inferring one ormore physical characteristics of an individual) from a DNA sample, itmay be desirable to utilize SNPs that affect the encoded protein.

For many of the SNPs disclosed in Tables 1-2 (which are identified as“Applera” SNP source), Tables 1-2 provide SNP allele frequenciesobtained by re-sequencing the DNA of chromosomes from 39 individuals(Tables 1-2 also provide allele frequency information for “Celera”source SNPs and, where available, public SNPs from dbEST, HGBASE, and/orHGMD). The allele frequencies provided in Tables 1-2 enable these SNPsto be readily used for human identification applications. Although anySNP disclosed in Table 1 and/or Table 2 could be used for humanidentification, the closer that the frequency of the minor allele at aparticular SNP site is to 50%, the greater the ability of that SNP todiscriminate between different individuals in a population since itbecomes increasingly likely that two randomly selected individuals wouldhave different alleles at that SNP site. Using the SNP allelefrequencies provided in Tables 1-2, one of ordinary skill in the artcould readily select a subset of SNPs for which the frequency of theminor allele is, for example, at least 1%, 2%, 5%, 10%, 20%, 25%, 30%,40%, 45%, or 50%, or any other frequency in-between. Thus, since Tables1-2 provide allele frequencies based on the re-sequencing of thechromosomes from 39 individuals, a subset of SNPs could readily beselected for human identification in which the total allele count of theminor allele at a particular SNP site is, for example, at least 1, 2, 4,8, 10, 16, 20, 24, 30, 32, 36, 38, 39, 40, or any other numberin-between.

Furthermore, Tables 1-2 also provide population group (interchangeablyreferred to herein as ethnic or racial groups) information coupled withthe extensive allele frequency information. For example, the group of 39individuals whose DNA was re-sequenced was made-up of 20 Caucasians and19 African-Americans. This population group information enables furtherrefinement of SNP selection for human identification. For example,preferred SNPs for human identification can be selected from Tables 1-2that have similar allele frequencies in both the Caucasian andAfrican-American populations; thus, for example, SNPs can be selectedthat have equally high discriminatory power in both populations.Alternatively, SNPs can be selected for which there is a statisticallysignificant difference in allele frequencies between the Caucasian andAfrican-American populations (as an extreme example, a particular allelemay be observed only in either the Caucasian or the African-Americanpopulation group but not observed in the other population group); suchSNPs are useful, for example, for predicting the race/ethnicity of anunknown perpetrator from a biological sample such as a hair or bloodstain recovered at a crime scene. For a discussion of using SNPs topredict ancestry from a DNA sample, including statistical methods, seeFrudakis et al., “A Classifier for the SNP-Based Inference of Ancestry”,Journal of Forensic Sciences 2003; 48(4):771-782.

SNPs have numerous advantages over other types of polymorphic markers,such as short tandem repeats (STRs). For example, SNPs can be easilyscored and are amenable to automation, making SNPs the markers of choicefor large-scale forensic databases. SNPs are found in much greaterabundance throughout the genome than repeat polymorphisms. Populationfrequencies of two polymorphic forms can usually be determined withgreater accuracy than those of multiple polymorphic forms atmulti-allelic loci. SNPs are mutationaly more stable than repeatpolymorphisms. SNPs are not susceptible to artefacts such as stutterbands that can hinder analysis. Stutter bands are frequently encounteredwhen analyzing repeat polymorphisms, and are particularly troublesomewhen analyzing samples such as crime scene samples that may containmixtures of DNA from multiple sources. Another significant advantage ofSNP markers over STR markers is the much shorter length of nucleic acidneeded to score a SNP. For example, STR markers are generally severalhundred base pairs in length. A SNP, on the other hand, comprises asingle nucleotide, and generally a short conserved region on either sideof the SNP position for primer and/or probe binding. This makes SNPsmore amenable to typing in highly degraded or aged biological samplesthat are frequently encountered in forensic casework in which DNA may befragmented into short pieces.

SNPs also are not subject to microvariant and “off-ladder” allelesfrequently encountered when analyzing STR loci. Microvariants aredeletions or insertions within a repeat unit that change the size of theamplified DNA product so that the amplified product does not migrate atthe same rate as reference alleles with normal sized repeat units. Whenseparated by size, such as by electrophoresis on a polyacrylamide gel,microvariants do not align with a reference allelic ladder of standardsized repeat units, but rather migrate between the reference alleles.The reference allelic ladder is used for precise sizing of alleles forallele classification; therefore alleles that do not align with thereference allelic ladder lead to substantial analysis problems.Furthermore, when analyzing multi-allelic repeat polymorphisms,occasionally an allele is found that consists of more or less repeatunits than has been previously seen in the population, or more or lessrepeat alleles than are included in a reference allelic ladder. Thesealleles will migrate outside the size range of known alleles in areference allelic ladder, and therefore are referred to as “off-ladder”alleles. In extreme cases, the allele may contain so few or so manyrepeats that it migrates well out of the range of the reference allelicladder. In this situation, the allele may not even be observed, or, withmultiplex analysis, it may migrate within or close to the size range foranother locus, further confounding analysis.

SNP analysis avoids the problems of microvariants and off-ladder allelesencountered in STR analysis. Importantly, microvariants and off-ladderalleles may provide significant problems, and may be completely missed,when using analysis methods such as oligonucleotide hybridizationarrays, which utilize oligonucleotide probes specific for certain knownalleles. Furthermore, off-ladder alleles and microvariants encounteredwith STR analysis, even when correctly typed, may lead to improperstatistical analysis, since their frequencies in the population aregenerally unknown or poorly characterized, and therefore the statisticalsignificance of a matching genotype may be questionable. All theseadvantages of SNP analysis are considerable in light of the consequencesof most DNA identification cases, which may lead to life imprisonmentfor an individual, or re-association of remains to the family of adeceased individual.

DNA can be isolated from biological samples such as blood, bone, hair,saliva, or semen, and compared with the DNA from a reference source atparticular SNP positions. Multiple SNP markers can be assayedsimultaneously in order to increase the power of discrimination and thestatistical significance of a matching genotype. For example,oligonucleotide arrays can be used to genotype a large number of SNPssimultaneously. The SNPs provided by the present invention can beassayed in combination with other polymorphic genetic markers, such asother SNPs known in the art or STRs, in order to identify an individualor to associate an individual with a particular biological sample.

Furthermore, the SNPs provided by the present invention can be genotypedfor inclusion in a database of DNA genotypes, for example, a criminalDNA databank such as the FBI's Combined DNA Index System (CODIS)database. A genotype obtained from a biological sample of unknown sourcecan then be queried against the database to find a matching genotype,with the SNPs of the present invention providing nucleotide positions atwhich to compare the known and unknown DNA sequences for identity.Accordingly, the present invention provides a database comprising novelSNPs or SNP alleles of the present invention (e.g., the database cancomprise information indicating which alleles are possessed byindividual members of a population at one or more novel SNP sites of thepresent invention), such as for use in forensics, biometrics, or otherhuman identification applications. Such a database typically comprises acomputer-based system in which the SNPs or SNP alleles of the presentinvention are recorded on a computer readable medium (see the section ofthe present specification entitled “Computer-Related Embodiments”).

The SNPs of the present invention can also be assayed for use inpaternity testing. The object of paternity testing is usually todetermine whether a male is the father of a child. In most cases, themother of the child is known and thus, the mother's contribution to thechild's genotype can be traced. Paternity testing investigates whetherthe part of the child's genotype not attributable to the mother isconsistent with that of the putative father. Paternity testing can beperformed by analyzing sets of polymorphisms in the putative father andthe child, with the SNPs of the present invention providing nucleotidepositions at which to compare the putative father's and child's DNAsequences for identity. If the set of polymorphisms in the childattributable to the father does not match the set of polymorphisms ofthe putative father, it can be concluded, barring experimental error,that the putative father is not the father of the child. If the set ofpolymorphisms in the child attributable to the father match the set ofpolymorphisms of the putative father, a statistical calculation can beperformed to determine the probability of coincidental match, and aconclusion drawn as to the likelihood that the putative father is thetrue biological father of the child.

In addition to paternity testing, SNPs are also useful for other typesof kinship testing, such as for verifying familial relationships forimmigration purposes, or for cases in which an individual alleges to berelated to a deceased individual in order to claim an inheritance fromthe deceased individual, etc. For further information regarding theutility of SNPs for paternity testing and other types of kinshiptesting, including methods for statistical analysis, see Krawczak,“Informativity assessment for biallelic single nucleotidepolymorphisms”, Electrophoresis 1999 June; 20(8):1676-81.

The use of the SNPs of the present invention for human identificationfurther extends to various authentication systems, commonly referred toas biometric systems, which typically convert physical characteristicsof humans (or other organisms) into digital data. Biometric systemsinclude various technological devices that measure such uniqueanatomical or physiological characteristics as finger, thumb, or palmprints; hand geometry; vein patterning on the back of the hand; bloodvessel patterning of the retina and color and texture of the iris;facial characteristics; voice patterns; signature and typing dynamics;and DNA. Such physiological measurements can be used to verify identityand, for example, restrict or allow access based on the identification.Examples of applications for biometrics include physical area security,computer and network security, aircraft passenger check-in and boarding,financial transactions, medical records access, government benefitdistribution, voting, law enforcement, passports, visas and immigration,prisons, various military applications, and for restricting access toexpensive or dangerous items, such as automobiles or guns (see, forexample, O'Connor, Stanford Technology Law Review and U.S. Pat. No.6,119,096).

Groups of SNPs, particularly the SNPs provided by the present invention,can be typed to uniquely identify an individual for biometricapplications such as those described above. Such SNP typing can readilybe accomplished using, for example, DNA chips/arrays. Preferably, aminimally invasive means for obtaining a DNA sample is utilized. Forexample, PCR amplification enables sufficient quantities of DNA foranalysis to be obtained from buccal swabs or fingerprints, which containDNA-containing skin cells and oils that are naturally transferred duringcontact. Further information regarding techniques for using SNPs inforensic/human identification applications can be found in, for example,Current Protocols in Human Genetics, John Wiley & Sons, N.Y. (2002),14.1-14.7.

Variant Proteins, Antibodies, Vectors & Host Cells, & Uses Thereof

Variant Proteins Encoded by SNP-Containing Nucleic Acid Molecules

The present invention provides SNP-containing nucleic acid molecules,many of which encode proteins having variant amino acid sequences ascompared to the art-known (i.e., wild-type) proteins. Amino acidsequences encoded by the polymorphic nucleic acid molecules of thepresent invention are provided as SEQ ID NOS:4-6 in Table 1 and theSequence Listing. These variants will generally be referred to herein asvariant proteins/peptides/polypeptides, or polymorphicproteins/peptides/polypeptides of the present invention. The terms“protein”, “peptide”, and “polypeptide” are used herein interchangeably.

A variant protein of the present invention may be encoded by, forexample, a nonsynonymous nucleotide substitution at any one of the cSNPpositions disclosed herein. In addition, variant proteins may alsoinclude proteins whose expression, structure, and/or function is alteredby a SNP disclosed herein, such as a SNP that creates or destroys a stopcodon, a SNP that affects splicing, and a SNP in control/regulatoryelements, e.g. promoters, enhancers, or transcription factor bindingdomains.

As used herein, a protein or peptide is said to be “isolated” or“purified” when it is substantially free of cellular material orchemical precursors or other chemicals. The variant proteins of thepresent invention can be purified to homogeneity or other lower degreesof purity. The level of purification will be based on the intended use.The key feature is that the preparation allows for the desired functionof the variant protein, even if in the presence of considerable amountsof other components.

As used herein, “substantially free of cellular material” includespreparations of the variant protein having less than about 30% (by dryweight) other proteins (i.e., contaminating protein), less than about20% other proteins, less than about 10% other proteins, or less thanabout 5% other proteins. When the variant protein is recombinantlyproduced, it can also be substantially free of culture medium, i.e.,culture medium represents less than about 20% of the volume of theprotein preparation.

The language “substantially free of chemical precursors or otherchemicals” includes preparations of the variant protein in which it isseparated from chemical precursors or other chemicals that are involvedin its synthesis. In one embodiment, the language “substantially free ofchemical precursors or other chemicals” includes preparations of thevariant protein having less than about 30% (by dry weight) chemicalprecursors or other chemicals, less than about 20% chemical precursorsor other chemicals, less than about 10% chemical precursors or otherchemicals, or less than about 5% chemical precursors or other chemicals.

An isolated variant protein may be purified from cells that naturallyexpress it, purified from cells that have been altered to express it(recombinant host cells), or synthesized using known protein synthesismethods. For example, a nucleic acid molecule containing SNP(s) encodingthe variant protein can be cloned into an expression vector, theexpression vector introduced into a host cell, and the variant proteinexpressed in the host cell. The variant protein can then be isolatedfrom the cells by any appropriate purification scheme using standardprotein purification techniques. Examples of these techniques aredescribed in detail below (Sambrook and Russell, 2000, MolecularCloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, ColdSpring Harbor, N.Y.).

The present invention provides isolated variant proteins that comprise,consist of or consist essentially of amino acid sequences that containone or more variant amino acids encoded by one or more codons whichcontain a SNP of the present invention.

Accordingly, the present invention provides variant proteins thatconsist of amino acid sequences that contain one or more amino acidpolymorphisms (or truncations or extensions due to creation ordestruction of a stop codon, respectively) encoded by the SNPs providedin Table 1 and/or Table 2. A protein consists of an amino acid sequencewhen the amino acid sequence is the entire amino acid sequence of theprotein.

The present invention further provides variant proteins that consistessentially of amino acid sequences that contain one or more amino acidpolymorphisms (or truncations or extensions due to creation ordestruction of a stop codon, respectively) encoded by the SNPs providedin Table 1 and/or Table 2. A protein consists essentially of an aminoacid sequence when such an amino acid sequence is present with only afew additional amino acid residues in the final protein.

The present invention further provides variant proteins that compriseamino acid sequences that contain one or more amino acid polymorphisms(or truncations or extensions due to creation or destruction of a stopcodon, respectively) encoded by the SNPs provided in Table 1 and/orTable 2. A protein comprises an amino acid sequence when the amino acidsequence is at least part of the final amino acid sequence of theprotein. In such a fashion, the protein may contain only the variantamino acid sequence or have additional amino acid residues, such as acontiguous encoded sequence that is naturally associated with it orheterologous amino acid residues. Such a protein can have a fewadditional amino acid residues or can comprise many more additionalamino acids. A brief description of how various types of these proteinscan be made and isolated is provided below.

The variant proteins of the present invention can be attached toheterologous sequences to form chimeric or fusion proteins. Suchchimeric and fusion proteins comprise a variant protein operativelylinked to a heterologous protein having an amino acid sequence notsubstantially homologous to the variant protein. “Operatively linked”indicates that the coding sequences for the variant protein and theheterologous protein are ligated in-frame. The heterologous protein canbe fused to the N-terminus or C-terminus of the variant protein. Inanother embodiment, the fusion protein is encoded by a fusionpolynucleotide that is synthesized by conventional techniques includingautomated DNA synthesizers. Alternatively, PCR amplification of genefragments can be carried out using anchor primers which give rise tocomplementary overhangs between two consecutive gene fragments which cansubsequently be annealed and re-amplified to generate a chimeric genesequence (see Ausubel et al., Current Protocols in Molecular Biology,1992). Moreover, many expression vectors are commercially available thatalready encode a fusion moiety (e.g., a GST protein). A variantprotein-encoding nucleic acid can be cloned into such an expressionvector such that the fusion moiety is linked in-frame to the variantprotein.

In many uses, the fusion protein does not affect the activity of thevariant protein. The fusion protein can include, but is not limited to,enzymatic fusion proteins, for example, beta-galactosidase fusions,yeast two-hybrid GAL fusions, poly-His fusions, MYC-tagged, HI-taggedand Ig fusions. Such fusion proteins, particularly poly-His fusions, canfacilitate their purification following recombinant expression. Incertain host cells (e.g., mammalian host cells), expression and/orsecretion of a protein can be increased by using a heterologous signalsequence. Fusion proteins are further described in, for example, Terpe,“Overview of tag protein fusions: from molecular and biochemicalfundamentals to commercial systems”, Appl Microbiol Biotechnol. 2003January;60(5):523-33. Epub 2002 Nov. 7; Graddis et al., “Designingproteins that work using recombinant technologies”, Curr PharmBiotechnol. 2002 December; 3(4):285-97; and Nilsson et al., “Affinityfusion strategies for detection, purification, and immobilization ofrecombinant proteins”, Protein Expr Purif. 1997 October; 11(1):1-16.

The present invention also relates to further obvious variants of thevariant polypeptides of the present invention, such asnaturally-occurring mature forms (e.g., alleleic variants),non-naturally occurring recombinantly-derived variants, and orthologsand paralogs of such proteins that share sequence homology. Suchvariants can readily be generated using art-known techniques in thefields of recombinant nucleic acid technology and protein biochemistry.It is understood, however, that variants exclude those known in theprior art before the present invention.

Further variants of the variant polypeptides disclosed in Table 1 cancomprise an amino acid sequence that shares at least 70-80%, 80-85%,85-90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% sequence identitywith an amino acid sequence disclosed in Table 1 (or a fragment thereof)and that includes a novel amino acid residue (allele) disclosed in Table1 (which is encoded by a novel SNP allele). Thus, an aspect of thepresent invention that is specifically contemplated are polypeptidesthat have a certain degree of sequence variation compared with thepolypeptide sequences shown in Table 1, but that contain a novel aminoacid residue (allele) encoded by a novel SNP allele disclosed herein. Inother words, as long as a polypeptide contains a novel amino acidresidue disclosed herein, other portions of the polypeptide that flankthe novel amino acid residue can vary to some degree from thepolypeptide sequences shown in Table 1.

Full-length pre-processed forms, as well as mature processed forms, ofproteins that comprise one of the amino acid sequences disclosed hereincan readily be identified as having complete sequence identity to one ofthe variant proteins of the present invention as well as being encodedby the same genetic locus as the variant proteins provided herein.

Orthologs of a variant peptide can readily be identified as having somedegree of significant sequence homology/identity to at least a portionof a variant peptide as well as being encoded by a gene from anotherorganism. Preferred orthologs will be isolated from non-human mammals,preferably primates, for the development of human therapeutic targetsand agents. Such orthologs can be encoded by a nucleic acid sequencethat hybridizes to a variant peptide-encoding nucleic acid moleculeunder moderate to stringent conditions depending on the degree ofrelatedness of the two organisms yielding the homologous proteins.

Variant proteins include, but are not limited to, proteins containingdeletions, additions and substitutions in the amino acid sequence causedby the SNPs of the present invention. One class of substitutions isconserved amino acid substitutions in which a given amino acid in apolypeptide is substituted for another amino acid of likecharacteristics. Typical conservative substitutions are replacements,one for another, among the aliphatic amino acids Ala, Val, Leu, and Be;interchange of the hydroxyl residues Ser and Thr; exchange of the acidicresidues Asp and Glu; substitution between the amide residues Asn andGln; exchange of the basic residues Lys and Arg; and replacements amongthe aromatic residues Phe and Tyr. Guidance concerning which amino acidchanges are likely to be phenotypically silent are found in, forexample, Bowie et al., Science 247:1306-1310 (1990).

Variant proteins can be fully functional or can lack function in one ormore activities, e.g. ability to bind another molecule, ability tocatalyze a substrate, ability to mediate signaling, etc. Fullyfunctional variants typically contain only conservative variations orvariations in non-critical residues or in non-critical regions.Functional variants can also contain substitution of similar amino acidsthat result in no change or an insignificant change in function.Alternatively, such substitutions may positively or negatively affectfunction to some degree. Non-functional variants typically contain oneor more non-conservative amino acid substitutions, deletions,insertions, inversions, truncations or extensions, or a substitution,insertion, inversion, or deletion of a critical residue or in a criticalregion.

Amino acids that are essential for function of a protein can beidentified by methods known in the art, such as site-directedmutagenesis or alanine-scanning mutagenesis (Cunningham et al., Science244:1081-1085 (1989)), particularly using the amino acid sequence andpolymorphism information provided in Table 1. The latter procedureintroduces single alanine mutations at every residue in the molecule.The resulting mutant molecules are then tested for biological activitysuch as enzyme activity or in assays such as an in vitro proliferativeactivity. Sites that are critical for binding partner/substrate bindingcan also be determined by structural analysis such as crystallization,nuclear magnetic resonance or photoaffinity labeling (Smith et al., J.Mol. Biol. 224:899-904 (1992); de Vos et al. Science 255:306-312(1992)).

Polypeptides can contain amino acids other than the 20 amino acidscommonly referred to as the 20 naturally occurring amino acids. Further,many amino acids, including the terminal amino acids, may be modified bynatural processes, such as processing and other post-translationalmodifications, or by chemical modification techniques well known in theart. Accordingly, the variant proteins of the present invention alsoencompass derivatives or analogs in which a substituted amino acidresidue is not one encoded by the genetic code, in which a substituentgroup is included, in which the mature polypeptide is fused with anothercompound, such as a compound to increase the half-life of thepolypeptide (e.g., polyethylene glycol), or in which additional aminoacids are fused to the mature polypeptide, such as a leader or secretorysequence or a sequence for purification of the mature polypeptide or apro-protein sequence.

Known protein modifications include, but are not limited to,acetylation, acylation, ADP-ribosylation, amidation, covalent attachmentof flavin, covalent attachment of a heme moiety, covalent attachment ofa nucleotide or nucleotide derivative, covalent attachment of a lipid orlipid derivative, covalent attachment of phosphotidylinositol,cross-linking, cyclization, disulfide bond formation, demethylation,formation of covalent crosslinks, formation of cystine, formation ofpyroglutamate, formylation, gamma carboxylation, glycosylation, GPIanchor formation, hydroxylation, iodination, methylation,myristoylation, oxidation, proteolytic processing, phosphorylation,prenylation, racemization, selenoylation, sulfation, transfer-RNAmediated addition of amino acids to proteins such as arginylation, andubiquitination.

Such protein modifications are well known to those of skill in the artand have been described in great detail in the scientific literature.Several particularly common modifications, glycosylation, lipidattachment, sulfation, gamma-carboxylation of glutamic acid residues,hydroxylation and ADP-ribosylation, for instance, are described in mostbasic texts, such as Proteins—Structure and Molecular Properties, 2ndEd., T. E. Creighton, W. H. Freeman and Company, New York (1993); Wold,F., Posttranslational Covalent Modification of Proteins, B. C. Johnson,Ed., Academic Press, New York 1-12 (1983); Seifter et al., Meth.Enzymol. 182: 626-646 (1990); and Rattan et al., Ann. N.Y. Acad. Sci.663:48-62 (1992).

The present invention further provides fragments of the variant proteinsin which the fragments contain one or more amino acid sequencevariations (e.g., substitutions, or truncations or extensions due tocreation or destruction of a stop codon) encoded by one or more SNPsdisclosed herein. The fragments to which the invention pertains,however, are not to be construed as encompassing fragments that havebeen disclosed in the prior art before the present invention.

As used herein, a fragment may comprise at least about 4, 8, 10, 12, 14,16, 18, 20, 25, 30, 50, 100 (or any other number in-between) or morecontiguous amino acid residues from a variant protein, wherein at leastone amino acid residue is affected by a SNP of the present invention,e.g., a variant amino acid residue encoded by a nonsynonymous nucleotidesubstitution at a cSNP position provided by the present invention. Thevariant amino acid encoded by a cSNP may occupy any residue positionalong the sequence of the fragment. Such fragments can be chosen basedon the ability to retain one or more of the biological activities of thevariant protein or the ability to perform a function, e.g., act as animmunogen. Particularly important fragments are biologically activefragments. Such fragments will typically comprise a domain or motif of avariant protein of the present invention, e.g., active site,transmembrane domain, or ligand/substrate binding domain. Otherfragments include, but are not limited to, domain or motif-containingfragments, soluble peptide fragments, and fragments containingimmunogenic structures. Predicted domains and functional sites arereadily identifiable by computer programs well known to those of skillin the art (e.g., PROSITE analysis) (Current Protocols in ProteinScience, John Wiley & Sons, N.Y. (2002)).

Uses of Variant Proteins

The variant proteins of the present invention can be used in a varietyof ways, including but not limited to, in assays to determine thebiological activity of a variant protein, such as in a panel of multipleproteins for high-throughput screening; to raise antibodies or to elicitanother type of immune response; as a reagent (including the labeledreagent) in assays designed to quantitatively determine levels of thevariant protein (or its binding partner) in biological fluids; as amarker for cells or tissues in which it is preferentially expressed(either constitutively or at a particular stage of tissuedifferentiation or development or in a disease state); as a target forscreening for a therapeutic agent; and as a direct therapeutic agent tobe administered into a human subject. Any of the variant proteinsdisclosed herein may be developed into reagent grade or kit format forcommercialization as research products. Methods for performing the useslisted above are well known to those skilled in the art (see, e.g.,Molecular Cloning: A Laboratory Manual, Cold Spring Harbor LaboratoryPress, Sambrook and Russell, 2000, and Methods in Enzymology: Guide toMolecular Cloning Techniques, Academic Press, Berger, S. L. and A. R.Kimmel eds., 1987).

In a specific embodiment of the invention, the methods of the presentinvention include detection of one or more variant proteins disclosedherein. Variant proteins are disclosed in Table 1 and in the SequenceListing as SEQ ID NOS:4-6. Detection of such proteins can beaccomplished using, for example, antibodies, small molecule compounds,aptamers, ligands/substrates, other proteins or protein fragments, orother protein-binding agents. Preferably, protein detection agents arespecific for a variant protein of the present invention and cantherefore discriminate between a variant protein of the presentinvention and the wild-type protein or another variant form. This cangenerally be accomplished by, for example, selecting or designingdetection agents that bind to the region of a protein that differsbetween the variant and wild-type protein, such as a region of a proteinthat contains one or more amino acid substitutions that is/are encodedby a non-synonymous cSNP of the present invention, or a region of aprotein that follows a nonsense mutation-type SNP that creates a stopcodon thereby leading to a shorter polypeptide, or a region of a proteinthat follows a read-through mutation-type SNP that destroys a stop codonthereby leading to a longer polypeptide in which a portion of thepolypeptide is present in one version of the polypeptide but not theother.

In another specific aspect of the invention, the variant proteins of thepresent invention are used as targets for diagnosing psoriasis or fordetermining predisposition to psoriasis in a human. Accordingly, theinvention provides methods for detecting the presence of, or levels of,one or more variant proteins of the present invention in a cell, tissue,or organism. Such methods typically involve contacting a test samplewith an agent (e.g., an antibody, small molecule compound, or peptide)capable of interacting with the variant protein such that specificbinding of the agent to the variant protein can be detected. Such anassay can be provided in a single detection format or a multi-detectionformat such as an array, for example, an antibody or aptamer array(arrays for protein detection may also be referred to as “proteinchips”). The variant protein of interest can be isolated from a testsample and assayed for the presence of a variant amino acid sequenceencoded by one or more SNPs disclosed by the present invention. The SNPsmay cause changes to the protein and the corresponding proteinfunction/activity, such as through non-synonymous substitutions inprotein coding regions that can lead to amino acid substitutions,deletions, insertions, and/or rearrangements; formation or destructionof stop codons; or alteration of control elements such as promoters.SNPs may also cause inappropriate post-translational modifications.

One preferred agent for detecting a variant protein in a sample is anantibody capable of selectively binding to a variant form of the protein(antibodies are described in greater detail in the next section). Suchsamples include, for example, tissues, cells, and biological fluidsisolated from a subject, as well as tissues, cells and fluids presentwithin a subject.

In vitro methods for detection of the variant proteins associated withpsoriasis that are disclosed herein and fragments thereof include, butare not limited to, enzyme linked immunosorbent assays (ELISAs),radioimmunoassays (RIA), Western blots, immunoprecipitations,immunofluorescence, and protein arrays/chips (e.g., arrays of antibodiesor aptamers). For further information regarding immunoassays and relatedprotein detection methods, see Current Protocols in Immunology, JohnWiley & Sons, N.Y., and Hage, “Immunoassays”, Anal Chem. 1999 Jun. 15;71(12):294R-304R.

Additional analytic methods of detecting amino acid variants include,but are not limited to, altered electrophoretic mobility, alteredtryptic peptide digest, altered protein activity in cell-based orcell-free assay, alteration in ligand or antibody-binding pattern,altered isoelectric point, and direct amino acid sequencing.

Alternatively, variant proteins can be detected in vivo in a subject byintroducing into the subject a labeled antibody (or other type ofdetection reagent) specific for a variant protein. For example, theantibody can be labeled with a radioactive marker whose presence andlocation in a subject can be detected by standard imaging techniques.

Other uses of the variant peptides of the present invention are based onthe class or action of the protein. For example, proteins isolated fromhumans and their mammalian orthologs serve as targets for identifyingagents (e.g., small molecule drugs or antibodies) for use in therapeuticapplications, particularly for modulating a biological or pathologicalresponse in a cell or tissue that expresses the protein. Pharmaceuticalagents can be developed that modulate protein activity.

As an alternative to modulating gene expression, therapeutic compoundscan be developed that modulate protein function. For example, many SNPsdisclosed herein affect the amino acid sequence of the encoded protein(e.g., non-synonymous cSNPs and nonsense mutation-type SNPs). Suchalterations in the encoded amino acid sequence may affect proteinfunction, particularly if such amino acid sequence variations occur infunctional protein domains, such as catalytic domains, ATP-bindingdomains, or ligand/substrate binding domains. It is well established inthe art that variant proteins having amino acid sequence variations infunctional domains can cause or influence pathological conditions. Insuch instances, compounds (e.g., small molecule drugs or antibodies) canbe developed that target the variant protein and modulate (e.g., up- ordown-regulate) protein function/activity.

The therapeutic methods of the present invention further include methodsthat target one or more variant proteins of the present invention.Variant proteins can be targeted using, for example, small moleculecompounds, antibodies, aptamers, ligands/substrates, other proteins, orother protein-binding agents. Additionally, the skilled artisan willrecognize that the novel protein variants (and polymorphic nucleic acidmolecules) disclosed in Table 1 may themselves be directly used astherapeutic agents by acting as competitive inhibitors of correspondingart-known proteins (or nucleic acid molecules such as mRNA molecules).

The variant proteins of the present invention are particularly useful indrug screening assays, in cell-based or cell-free systems. Cell-basedsystems can utilize cells that naturally express the protein, a biopsyspecimen, or cell cultures. In one embodiment, cell-based assays involverecombinant host cells expressing the variant protein. Cell-free assayscan be used to detect the ability of a compound to directly bind to avariant protein or to the corresponding SNP-containing nucleic acidfragment that encodes the variant protein.

A variant protein of the present invention, as well as appropriatefragments thereof, can be used in high-throughput screening assays totest candidate compounds for the ability to bind and/or modulate theactivity of the variant protein. These candidate compounds can befurther screened against a protein having normal function (e.g., awild-type/non-variant protein) to further determine the effect of thecompound on the protein activity. Furthermore, these compounds can betested in animal or invertebrate systems to determine in vivoactivity/effectiveness. Compounds can be identified that activate(agonists) or inactivate (antagonists) the variant protein, anddifferent compounds can be identified that cause various degrees ofactivation or inactivation of the variant protein.

Further, the variant proteins can be used to screen a compound for theability to stimulate or inhibit interaction between the variant proteinand a target molecule that normally interacts with the protein. Thetarget can be a ligand, a substrate or a binding partner that theprotein normally interacts with (for example, epinephrine ornorepinephrine). Such assays typically include the steps of combiningthe variant protein with a candidate compound under conditions thatallow the variant protein, or fragment thereof, to interact with thetarget molecule, and to detect the formation of a complex between theprotein and the target or to detect the biochemical consequence of theinteraction with the variant protein and the target, such as any of theassociated effects of signal transduction.

Candidate compounds include, for example, 1) peptides such as solublepeptides, including Ig-tailed fusion peptides and members of randompeptide libraries (see, e.g., Lam et al., Nature 354:82-84 (1991);Houghten et al., Nature 354:84-86 (1991)) and combinatorialchemistry-derived molecular libraries made of D- and/or L-configurationamino acids; 2) phosphopeptides (e.g., members of random and partiallydegenerate, directed phosphopeptide libraries, see, e.g., Songyang etal., Cell 72:767-778 (1993)); 3) antibodies (e.g., polyclonal,monoclonal, humanized, anti-idiotypic, chimeric, and single chainantibodies as well as Fab, F(ab′)₂, Fab expression library fragments,and epitope-binding fragments of antibodies); and 4) small organic andinorganic molecules (e.g., molecules obtained from combinatorial andnatural product libraries).

One candidate compound is a soluble fragment of the variant protein thatcompetes for ligand binding. Other candidate compounds include mutantproteins or appropriate fragments containing mutations that affectvariant protein function and thus compete for ligand. Accordingly, afragment that competes for ligand, for example with a higher affinity,or a fragment that binds ligand but does not allow release, isencompassed by the invention.

The invention further includes other end point assays to identifycompounds that modulate (stimulate or inhibit) variant protein activity.The assays typically involve an assay of events in the signaltransduction pathway that indicate protein activity. Thus, theexpression of genes that are up or down-regulated in response to thevariant protein dependent signal cascade can be assayed. In oneembodiment, the regulatory region of such genes can be operably linkedto a marker that is easily detectable, such as luciferase.Alternatively, phosphorylation of the variant protein, or a variantprotein target, could also be measured. Any of the biological orbiochemical functions mediated by the variant protein can be used as anendpoint assay. These include all of the biochemical or biologicalevents described herein, in the references cited herein, incorporated byreference for these endpoint assay targets, and other functions known tothose of ordinary skill in the art.

Binding and/or activating compounds can also be screened by usingchimeric variant proteins in which an amino terminal extracellulardomain or parts thereof, an entire transmembrane domain or subregions,and/or the carboxyl terminal intracellular domain or parts thereof, canbe replaced by heterologous domains or subregions. For example, asubstrate-binding region can be used that interacts with a differentsubstrate than that which is normally recognized by a variant protein.Accordingly, a different set of signal transduction components isavailable as an end-point assay for activation. This allows for assaysto be performed in other than the specific host cell from which thevariant protein is derived.

The variant proteins are also useful in competition binding assays inmethods designed to discover compounds that interact with the variantprotein. Thus, a compound can be exposed to a variant protein underconditions that allow the compound to bind or to otherwise interact withthe variant protein. A binding partner, such as ligand, that normallyinteracts with the variant protein is also added to the mixture. If thetest compound interacts with the variant protein or its binding partner,it decreases the amount of complex formed or activity from the variantprotein. This type of assay is particularly useful in screening forcompounds that interact with specific regions of the variant protein(Hodgson, Bio/technology, 1992, Sep. 10(9), 973-80).

To perform cell-free drug screening assays, it is sometimes desirable toimmobilize either the variant protein or a fragment thereof, or itstarget molecule, to facilitate separation of complexes from uncomplexedforms of one or both of the proteins, as well as to accommodateautomation of the assay. Any method for immobilizing proteins onmatrices can be used in drug screening assays. In one embodiment, afusion protein containing an added domain allows the protein to be boundto a matrix. For example, glutathione-S-transferase/¹²⁵I fusion proteinscan be adsorbed onto glutathione sepharose beads (Sigma Chemical, St.Louis, Mo.) or glutathione derivatized microtitre plates, which are thencombined with the cell lysates (e.g., ³⁵S-labeled) and a candidatecompound, such as a drug candidate, and the mixture incubated underconditions conducive to complex formation (e.g., at physiologicalconditions for salt and pH). Following incubation, the beads can bewashed to remove any unbound label, and the matrix immobilized andradiolabel determined directly, or in the supernatant after thecomplexes are dissociated. Alternatively, the complexes can bedissociated from the matrix, separated by SDS-PAGE, and the level ofbound material found in the bead fraction quantitated from the gel usingstandard electrophoretic techniques.

Either the variant protein or its target molecule can be immobilizedutilizing conjugation of biotin and streptavidin. Alternatively,antibodies reactive with the variant protein but which do not interferewith binding of the variant protein to its target molecule can bederivatized to the wells of the plate, and the variant protein trappedin the wells by antibody conjugation. Preparations of the targetmolecule and a candidate compound are incubated in the variantprotein-presenting wells and the amount of complex trapped in the wellcan be quantitated. Methods for detecting such complexes, in addition tothose described above for the GST-immobilized complexes, includeimmunodetection of complexes using antibodies reactive with the proteintarget molecule, or which are reactive with variant protein and competewith the target molecule, and enzyme-linked assays that rely ondetecting an enzymatic activity associated with the target molecule.

Modulators of variant protein activity identified according to thesedrug screening assays can be used to treat a subject with a disordermediated by the protein pathway, such as psoriasis. These methods oftreatment typically include the steps of administering the modulators ofprotein activity in a pharmaceutical composition to a subject in need ofsuch treatment.

The variant proteins, or fragments thereof, disclosed herein canthemselves be directly used to treat a disorder characterized by anabsence of, inappropriate, or unwanted expression or activity of thevariant protein. Accordingly, methods for treatment include the use of avariant protein disclosed herein or fragments thereof.

In yet another aspect of the invention, variant proteins can be used as“bait proteins” in a two-hybrid assay or three-hybrid assay (see, e.g.,U.S. Pat. No. 5,283,317; Zervos et al. (1993) Cell 72:223-232; Madura etal. (1993) J. Biol. Chem. 268:12046-12054; Bartel et al. (1993)Biotechniques 14:920-924; Iwabuchi et al. (1993) Oncogene 8:1693-1696;and Brent WO94/10300) to identify other proteins that bind to orinteract with the variant protein and are involved in variant proteinactivity. Such variant protein-binding proteins are also likely to beinvolved in the propagation of signals by the variant proteins orvariant protein targets as, for example, elements of a protein-mediatedsignaling pathway. Alternatively, such variant protein-binding proteinsare inhibitors of the variant protein.

The two-hybrid system is based on the modular nature of mosttranscription factors, which typically consist of separable DNA-bindingand activation domains. Briefly, the assay typically utilizes twodifferent DNA constructs. In one construct, the gene that codes for avariant protein is fused to a gene encoding the DNA binding domain of aknown transcription factor (e.g., GAL-4). In the other construct, a DNAsequence, from a library of DNA sequences, that encodes an unidentifiedprotein (“prey” or “sample”) is fused to a gene that codes for theactivation domain of the known transcription factor. If the “bait” andthe “prey” proteins are able to interact, in vivo, forming a variantprotein-dependent complex, the DNA-binding and activation domains of thetranscription factor are brought into close proximity. This proximityallows transcription of a reporter gene (e.g., LacZ) that is operablylinked to a transcriptional regulatory site responsive to thetranscription factor. Expression of the reporter gene can be detected,and cell colonies containing the functional transcription factor can beisolated and used to obtain the cloned gene that encodes the proteinthat interacts with the variant protein.

Antibodies Directed to Variant Proteins

The present invention also provides antibodies that selectively bind tothe variant proteins disclosed herein and fragments thereof. Suchantibodies may be used to quantitatively or qualitatively detect thevariant proteins of the present invention. As used herein, an antibodyselectively binds a target variant protein when it binds the variantprotein and does not significantly bind to non-variant proteins, i.e.,the antibody does not significantly bind to normal, wild-type, orart-known proteins that do not contain a variant amino acid sequence dueto one or more SNPs of the present invention (variant amino acidsequences may be due to, for example, nonsynonymous cSNPs, nonsense SNPsthat create a stop codon, thereby causing a truncation of a polypeptideor SNPs that cause read-through mutations resulting in an extension of apolypeptide).

As used herein, an antibody is defined in terms consistent with thatrecognized in the art: they are multi-subunit proteins produced by anorganism in response to an antigen challenge. The antibodies of thepresent invention include both monoclonal antibodies and polyclonalantibodies, as well as antigen-reactive proteolytic fragments of suchantibodies, such as Fab, F(ab)′₂, and Fv fragments. In addition, anantibody of the present invention further includes any of a variety ofengineered antigen-binding molecules such as a chimeric antibody (U.S.Pat. Nos. 4,816,567 and 4,816,397; Morrison et al., Proc. Natl. Acad.Sci. USA, 81:6851, 1984; Neuberger et al., Nature 312:604, 1984), ahumanized antibody (U.S. Pat. Nos. 5,693,762; 5,585,089; and 5,565,332),a single-chain Fv (U.S. Pat. No. 4,946,778; Ward et al., Nature 334:544,1989), a bispecific antibody with two binding specificities (Segal etal., J. Immunol. Methods 248:1, 2001; Carter, J. Immunol. Methods 248:7,2001), a diabody, a triabody, and a tetrabody (Todorovska et al., J.Immunol. Methods, 248:47, 2001), as well as a Fab conjugate (dimer ortrimer), and a minibody.

Many methods are known in the art for generating and/or identifyingantibodies to a given target antigen (Harlow, Antibodies, Cold SpringHarbor Press, (1989)). In general, an isolated peptide (e.g., a variantprotein of the present invention) is used as an immunogen and isadministered to a mammalian organism, such as a rat, rabbit, hamster ormouse. Either a full-length protein, an antigenic peptide fragment(e.g., a peptide fragment containing a region that varies between avariant protein and a corresponding wild-type protein), or a fusionprotein can be used. A protein used as an immunogen may benaturally-occurring, synthetic or recombinantly produced, and may beadministered in combination with an adjuvant, including but not limitedto, Freund's (complete and incomplete), mineral gels such as aluminumhydroxide, surface active substance such as lysolecithin, pluronicpolyols, polyanions, peptides, oil emulsions, keyhole limpet hemocyanin,dinitrophenol, and the like.

Monoclonal antibodies can be produced by hybridoma technology (Kohlerand Milstein, Nature, 256:495, 1975), which immortalizes cells secretinga specific monoclonal antibody. The immortalized cell lines can becreated in vitro by fusing two different cell types, typicallylymphocytes, and tumor cells. The hybridoma cells may be cultivated invitro or in vivo. Additionally, fully human antibodies can be generatedby transgenic animals (He et al., J. Immunol., 169:595, 2002). Fd phageand Fd phagemid technologies may be used to generate and selectrecombinant antibodies in vitro (Hoogenboom and Chames, Immunol. Today21:371, 2000; Liu et al., J. Mol. Biol. 315:1063, 2002). Thecomplementarity-determining regions of an antibody can be identified,and synthetic peptides corresponding to such regions may be used tomediate antigen binding (U.S. Pat. No. 5,637,677).

Antibodies are preferably prepared against regions or discrete fragmentsof a variant protein containing a variant amino acid sequence ascompared to the corresponding wild-type protein (e.g., a region of avariant protein that includes an amino acid encoded by a nonsynonymouscSNP, a region affected by truncation caused by a nonsense SNP thatcreates a stop codon, or a region resulting from the destruction of astop codon due to read-through mutation caused by a SNP). Furthermore,preferred regions will include those involved in function/activityand/or protein/binding partner interaction. Such fragments can beselected on a physical property, such as fragments corresponding toregions that are located on the surface of the protein, e.g.,hydrophilic regions, or can be selected based on sequence uniqueness, orbased on the position of the variant amino acid residue(s) encoded bythe SNPs provided by the present invention. An antigenic fragment willtypically comprise at least about 8-10 contiguous amino acid residues inwhich at least one of the amino acid residues is an amino acid affectedby a SNP disclosed herein. The antigenic peptide can comprise, however,at least 12, 14, 16, 20, 25, 50, 100 (or any other number in-between) ormore amino acid residues, provided that at least one amino acid isaffected by a SNP disclosed herein.

Detection of an antibody of the present invention can be facilitated bycoupling (i.e., physically linking) the antibody or an antigen-reactivefragment thereof to a detectable substance. Detectable substancesinclude, but are not limited to, various enzymes, prosthetic groups,fluorescent materials, luminescent materials, bioluminescent materials,and radioactive materials. Examples of suitable enzymes includehorseradish peroxidase, alkaline phosphatase, β-galactosidase, oracetylcholinesterase; examples of suitable prosthetic group complexesinclude streptavidin/biotin and avidin/biotin; examples of suitablefluorescent materials include umbelliferone, fluorescein, fluoresceinisothiocyanate, rhodamine, dichlorotriazinylamine fluorescein, dansylchloride or phycoerythrin; an example of a luminescent material includesluminol; examples of bioluminescent materials include luciferase,luciferin, and aequorin, and examples of suitable radioactive materialinclude ¹²⁵I, ¹³¹I, ³⁵S or ³H.

Antibodies, particularly the use of antibodies as therapeutic agents,are reviewed in: Morgan, “Antibody therapy for Alzheimer's disease”,Expert Rev Vaccines. 2003 February; 2(1):53-9; Ross et al., “Anticancerantibodies”, Am J Clin Pathol. 2003 April; 119(4):472-85; Goldenberg,“Advancing role of radiolabeled antibodies in the therapy of cancer”,Cancer Immunol Immunother. 2003 May; 52(5):281-96. Epub 2003 Mar. 11;Ross et al., “Antibody-based therapeutics in oncology”, Expert RevAnticancer Ther. 2003 February; 3(1):107-21; Cao et al., “Bispecificantibody conjugates in therapeutics”, Adv Drug Deliv Rev. 2003 Feb. 10;55(2):171-97; von Mehren et al., “Monoclonal antibody therapy forcancer”, Annu Rev Med. 2003; 54:343-69. Epub 2001 Dec. 3; Hudson et al.,“Engineered antibodies”, Nat Med. 2003 January;9(1):129-34; Brekke etal., “Therapeutic antibodies for human diseases at the dawn of thetwenty-first century”, Nat Rev Drug Discov. 2003 January;2(1):52-62(Erratum in: Nat Rev Drug Discov. 2003 March; 2(3):240); Houdebine,“Antibody manufacture in transgenic animals and comparisons with othersystems”, Curr Opin Biotechnol. 2002 December; 13(6):625-9; Andreakos etal., “Monoclonal antibodies in immune and inflammatory diseases”, CurrOpin Biotechnol. 2002 December; 13(6):615-20; Kellermann et al.,“Antibody discovery: the use of transgenic mice to generate humanmonoclonal antibodies for therapeutics”, Curr Opin Biotechnol. 2002December; 13(6):593-7; Pini et al., “Phage display and colony filterscreening for high-throughput selection of antibody libraries”, CombChem High Throughput Screen. 2002 November; 5(7):503-10; Batra et al.,“Pharmacokinetics and biodistribution of genetically engineeredantibodies”, Curr Opin Biotechnol. 2002 December; 13(6):603-8; andTangri et al., “Rationally engineered proteins or antibodies with absentor reduced immunogenicity”, Curr Med. Chem. 2002 December; 9(24):2191-9.

Uses of Antibodies

Antibodies can be used to isolate the variant proteins of the presentinvention from a natural cell source or from recombinant host cells bystandard techniques, such as affinity chromatography orimmunoprecipitation. In addition, antibodies are useful for detectingthe presence of a variant protein of the present invention in cells ortissues to determine the pattern of expression of the variant proteinamong various tissues in an organism and over the course of normaldevelopment or disease progression. Further, antibodies can be used todetect variant protein in situ, in vitro, in a bodily fluid, or in acell lysate or supernatant in order to evaluate the amount and patternof expression. Also, antibodies can be used to assess abnormal tissuedistribution, abnormal expression during development, or expression inan abnormal condition, such as psoriasis. Additionally, antibodydetection of circulating fragments of the full-length variant proteincan be used to identify turnover.

Antibodies to the variant proteins of the present invention are alsouseful in pharmacogenomic analysis. Thus, antibodies against variantproteins encoded by alternative SNP alleles can be used to identifyindividuals that require modified treatment modalities.

Further, antibodies can be used to assess expression of the variantprotein in disease states such as in active stages of the disease or inan individual with a predisposition to a disease related to theprotein's function, particularly psoriasis. Antibodies specific for avariant protein encoded by a SNP-containing nucleic acid molecule of thepresent invention can be used to assay for the presence of the variantprotein, such as to screen for predisposition to psoriasis as indicatedby the presence of the variant protein.

Antibodies are also useful as diagnostic tools for evaluating thevariant proteins in conjunction with analysis by electrophoreticmobility, isoelectric point, tryptic peptide digest, and other physicalassays well known in the art.

Antibodies are also useful for tissue typing. Thus, where a specificvariant protein has been correlated with expression in a specifictissue, antibodies that are specific for this protein can be used toidentify a tissue type.

Antibodies can also be used to assess aberrant subcellular localizationof a variant protein in cells in various tissues. The diagnostic usescan be applied, not only in genetic testing, but also in monitoring atreatment modality. Accordingly, where treatment is ultimately aimed atcorrecting the expression level or the presence of variant protein oraberrant tissue distribution or developmental expression of a variantprotein, antibodies directed against the variant protein or relevantfragments can be used to monitor therapeutic efficacy.

The antibodies are also useful for inhibiting variant protein function,for example, by blocking the binding of a variant protein to a bindingpartner. These uses can also be applied in a therapeutic context inwhich treatment involves inhibiting a variant protein's function. Anantibody can be used, for example, to block or competitively inhibitbinding, thus modulating (agonizing or antagonizing) the activity of avariant protein. Antibodies can be prepared against specific variantprotein fragments containing sites required for function or against anintact variant protein that is associated with a cell or cell membrane.For in vivo administration, an antibody may be linked with an additionaltherapeutic payload such as a radionuclide, an enzyme, an immunogenicepitope, or a cytotoxic agent. Suitable cytotoxic agents include, butare not limited to, bacterial toxin such as diphtheria, and plant toxinsuch as ricin. The in vivo half-life of an antibody or a fragmentthereof may be lengthened by pegylation through conjugation topolyethylene glycol (Leong et al., Cytokine 16:106, 2001).

The invention also encompasses kits for using antibodies, such as kitsfor detecting the presence of a variant protein in a test sample. Anexemplary kit can comprise antibodies such as a labeled or labelableantibody and a compound or agent for detecting variant proteins in abiological sample; means for determining the amount, or presence/absenceof variant protein in the sample; means for comparing the amount ofvariant protein in the sample with a standard; and instructions for use.

Vectors and Host Cells

The present invention also provides vectors containing theSNP-containing nucleic acid molecules described herein. The term“vector” refers to a vehicle, preferably a nucleic acid molecule, whichcan transport a SNP-containing nucleic acid molecule. When the vector isa nucleic acid molecule, the SNP-containing nucleic acid molecule can becovalently linked to the vector nucleic acid. Such vectors include, butare not limited to, a plasmid, single or double stranded phage, a singleor double stranded RNA or DNA viral vector, or artificial chromosome,such as a BAC, PAC, YAC, or MAC.

A vector can be maintained in a host cell as an extrachromosomal elementwhere it replicates and produces additional copies of the SNP-containingnucleic acid molecules. Alternatively, the vector may integrate into thehost cell genome and produce additional copies of the SNP-containingnucleic acid molecules when the host cell replicates.

The invention provides vectors for the maintenance (cloning vectors) orvectors for expression (expression vectors) of the SNP-containingnucleic acid molecules. The vectors can function in prokaryotic oreukaryotic cells or in both (shuttle vectors).

Expression vectors typically contain cis-acting regulatory regions thatare operably linked in the vector to the SNP-containing nucleic acidmolecules such that transcription of the SNP-containing nucleic acidmolecules is allowed in a host cell. The SNP-containing nucleic acidmolecules can also be introduced into the host cell with a separatenucleic acid molecule capable of affecting transcription. Thus, thesecond nucleic acid molecule may provide a trans-acting factorinteracting with the cis-regulatory control region to allowtranscription of the SNP-containing nucleic acid molecules from thevector. Alternatively, a trans-acting factor may be supplied by the hostcell. Finally, a trans-acting factor can be produced from the vectoritself. It is understood, however, that in some embodiments,transcription and/or translation of the nucleic acid molecules can occurin a cell-free system.

The regulatory sequences to which the SNP-containing nucleic acidmolecules described herein can be operably linked include promoters fordirecting mRNA transcription. These include, but are not limited to, theleft promoter from bacteriophage λ, the lac, TRP, and TAC promoters fromE. coli, the early and late promoters from SV40, the CMV immediate earlypromoter, the adenovirus early and late promoters, and retroviruslong-terminal repeats.

In addition to control regions that promote transcription, expressionvectors may also include regions that modulate transcription, such asrepressor binding sites and enhancers. Examples include the SV40enhancer, the cytomegalovirus immediate early enhancer, polyomaenhancer, adenovirus enhancers, and retrovirus LTR enhancers.

In addition to containing sites for transcription initiation andcontrol, expression vectors can also contain sequences necessary fortranscription termination and, in the transcribed region, aribosome-binding site for translation. Other regulatory control elementsfor expression include initiation and termination codons as well aspolyadenylation signals. A person of ordinary skill in the art would beaware of the numerous regulatory sequences that are useful in expressionvectors (see, e.g., Sambrook and Russell, 2000, Molecular Cloning: ALaboratory Manual, Cold Spring Harbor Laboratory Press, Cold SpringHarbor, N.Y.).

A variety of expression vectors can be used to express a SNP-containingnucleic acid molecule. Such vectors include chromosomal, episomal, andvirus-derived vectors, for example, vectors derived from bacterialplasmids, from bacteriophage, from yeast episomes, from yeastchromosomal elements, including yeast artificial chromosomes, fromviruses such as baculoviruses, papovaviruses such as SV40, Vacciniaviruses, adenoviruses, poxviruses, pseudorabies viruses, andretroviruses. Vectors can also be derived from combinations of thesesources such as those derived from plasmid and bacteriophage geneticelements, e.g., cosmids and phagemids. Appropriate cloning andexpression vectors for prokaryotic and eukaryotic hosts are described inSambrook and Russell, 2000, Molecular Cloning: A Laboratory Manual, ColdSpring Harbor Laboratory Press, Cold Spring Harbor, N.Y.

The regulatory sequence in a vector may provide constitutive expressionin one or more host cells (e.g., tissue specific expression) or mayprovide for inducible expression in one or more cell types such as bytemperature, nutrient additive, or exogenous factor, e.g., a hormone orother ligand. A variety of vectors that provide constitutive orinducible expression of a nucleic acid sequence in prokaryotic andeukaryotic host cells are well known to those of ordinary skill in theart.

A SNP-containing nucleic acid molecule can be inserted into the vectorby methodology well-known in the art. Generally, the SNP-containingnucleic acid molecule that will ultimately be expressed is joined to anexpression vector by cleaving the SNP-containing nucleic acid moleculeand the expression vector with one or more restriction enzymes and thenligating the fragments together. Procedures for restriction enzymedigestion and ligation are well known to those of ordinary skill in theart.

The vector containing the appropriate nucleic acid molecule can beintroduced into an appropriate host cell for propagation or expressionusing well-known techniques. Bacterial host cells include, but are notlimited to, E. coli, Streptomyces, and Salmonella typhimurium.Eukaryotic host cells include, but are not limited to, yeast, insectcells such as Drosophila, animal cells such as COS and CHO cells, andplant cells.

As described herein, it may be desirable to express the variant peptideas a fusion protein. Accordingly, the invention provides fusion vectorsthat allow for the production of the variant peptides. Fusion vectorscan, for example, increase the expression of a recombinant protein,increase the solubility of the recombinant protein, and aid in thepurification of the protein by acting, for example, as a ligand foraffinity purification. A proteolytic cleavage site may be introduced atthe junction of the fusion moiety so that the desired variant peptidecan ultimately be separated from the fusion moiety. Proteolytic enzymessuitable for such use include, but are not limited to, factor Xa,thrombin, and enterokinase. Typical fusion expression vectors includepGEX (Smith et al., Gene 67:31-40 (1988)), pMAL (New England Biolabs,Beverly, Mass.) and pRIT5 (Pharmacia, Piscataway, N.J.) which fuseglutathione S-transferase (GST), maltose E binding protein, or proteinA, respectively, to the target recombinant protein. Examples of suitableinducible non-fusion E. coli expression vectors include pTrc (Amann etal., Gene 69:301-315 (1988)) and pET 11d (Studier et al., GeneExpression Technology: Methods in Enzymology 185:60-89 (1990)).

Recombinant protein expression can be maximized in a bacterial host byproviding a genetic background wherein the host cell has an impairedcapacity to proteolytically cleave the recombinant protein (Gottesman,S., Gene Expression Technology: Methods in Enzymology 185, AcademicPress, San Diego, Calif. (1990) 119-128). Alternatively, the sequence ofthe SNP-containing nucleic acid molecule of interest can be altered toprovide preferential codon usage for a specific host cell, for example,E. coli (Wada et al., Nucleic Acids Res. 20:2111-2118 (1992)).

The SNP-containing nucleic acid molecules can also be expressed byexpression vectors that are operative in yeast. Examples of vectors forexpression in yeast (e.g., S. cerevisiae) include pYepSec1 (Baldari, etal., EMBO J. 6:229-234 (1987)), pMFa (Kurjan et al., Cell 30:933-943(1982)), pJRY88 (Schultz et al., Gene 54:113-123 (1987)), and pYES2(Invitrogen Corporation, San Diego, Calif.).

The SNP-containing nucleic acid molecules can also be expressed ininsect cells using, for example, baculovirus expression vectors.Baculovirus vectors available for expression of proteins in culturedinsect cells (e.g., Sf 9 cells) include the pAc series (Smith et al.,Mol. Cell Biol. 3:2156-2165 (1983)) and the pVL series (Lucklow et al.,Virology 170:31-39 (1989)).

In certain embodiments of the invention, the SNP-containing nucleic acidmolecules described herein are expressed in mammalian cells usingmammalian expression vectors. Examples of mammalian expression vectorsinclude pCDM8 (Seed, B. Nature 329:840 (1987)) and pMT2PC (Kaufman etal., EMBO J. 6:187-195 (1987)).

The invention also encompasses vectors in which the SNP-containingnucleic acid molecules described herein are cloned into the vector inreverse orientation, but operably linked to a regulatory sequence thatpermits transcription of antisense RNA. Thus, an antisense transcriptcan be produced to the SNP-containing nucleic acid sequences describedherein, including both coding and non-coding regions. Expression of thisantisense RNA is subject to each of the parameters described above inrelation to expression of the sense RNA (regulatory sequences,constitutive or inducible expression, tissue-specific expression).

The invention also relates to recombinant host cells containing thevectors described herein. Host cells therefore include, for example,prokaryotic cells, lower eukaryotic cells such as yeast, othereukaryotic cells such as insect cells, and higher eukaryotic cells suchas mammalian cells.

The recombinant host cells can be prepared by introducing the vectorconstructs described herein into the cells by techniques readilyavailable to persons of ordinary skill in the art. These include, butare not limited to, calcium phosphate transfection,DEAE-dextran-mediated transfection, cationic lipid-mediatedtransfection, electroporation, transduction, infection, lipofection, andother techniques such as those described in Sambrook and Russell, 2000,Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory,Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y.).

Host cells can contain more than one vector. Thus, differentSNP-containing nucleotide sequences can be introduced in differentvectors into the same cell. Similarly, the SNP-containing nucleic acidmolecules can be introduced either alone or with other nucleic acidmolecules that are not related to the SNP-containing nucleic acidmolecules, such as those providing trans-acting factors for expressionvectors. When more than one vector is introduced into a cell, thevectors can be introduced independently, co-introduced, or joined to thenucleic acid molecule vector.

In the case of bacteriophage and viral vectors, these can be introducedinto cells as packaged or encapsulated virus by standard procedures forinfection and transduction. Viral vectors can be replication-competentor replication-defective. In the case in which viral replication isdefective, replication can occur in host cells that provide functionsthat complement the defects.

Vectors generally include selectable markers that enable the selectionof the subpopulation of cells that contain the recombinant vectorconstructs. The marker can be inserted in the same vector that containsthe SNP-containing nucleic acid molecules described herein or may be ina separate vector. Markers include, for example, tetracycline orampicillin-resistance genes for prokaryotic host cells, anddihydrofolate reductase or neomycin resistance genes for eukaryotic hostcells. However, any marker that provides selection for a phenotypictrait can be effective.

While the mature variant proteins can be produced in bacteria, yeast,mammalian cells, and other cells under the control of the appropriateregulatory sequences, cell-free transcription and translation systemscan also be used to produce these variant proteins using RNA derivedfrom the DNA constructs described herein.

Where secretion of the variant protein is desired, which is difficult toachieve with multi-transmembrane domain containing proteins such asG-protein-coupled receptors (GPCRs), appropriate secretion signals canbe incorporated into the vector. The signal sequence can be endogenousto the peptides or heterologous to these peptides.

Where the variant protein is not secreted into the medium, the proteincan be isolated from the host cell by standard disruption procedures,including freeze/thaw, sonication, mechanical disruption, use of lysingagents, and the like. The variant protein can then be recovered andpurified by well-known purification methods including, for example,ammonium sulfate precipitation, acid extraction, anion or cationicexchange chromatography, phosphocellulose chromatography,hydrophobic-interaction chromatography, affinity chromatography,hydroxylapatite chromatography, lectin chromatography, or highperformance liquid chromatography.

It is also understood that, depending upon the host cell in whichrecombinant production of the variant proteins described herein occurs,they can have various glycosylation patterns, or may benon-glycosylated, as when produced in bacteria. In addition, the variantproteins may include an initial modified methionine in some cases as aresult of a host-mediated process.

For further information regarding vectors and host cells, see CurrentProtocols in Molecular Biology, John Wiley & Sons, N.Y.

Uses of Vectors and Host Cells, and Transgenic Animals

Recombinant host cells that express the variant proteins describedherein have a variety of uses.

For example, the cells are useful for producing a variant protein thatcan be further purified into a preparation of desired amounts of thevariant protein or fragments thereof. Thus, host cells containingexpression vectors are useful for variant protein production.

Host cells are also useful for conducting cell-based assays involvingthe variant protein or variant protein fragments, such as thosedescribed above as well as other formats known in the art. Thus, arecombinant host cell expressing a variant protein is useful forassaying compounds that stimulate or inhibit variant protein function.Such an ability of a compound to modulate variant protein function maynot be apparent from assays of the compound on the native/wild-typeprotein, or from cell-free assays of the compound. Recombinant hostcells are also useful for assaying functional alterations in the variantproteins as compared with a known function.

Genetically-engineered host cells can be further used to producenon-human transgenic animals. A transgenic animal is preferably anon-human mammal, for example, a rodent, such as a rat or mouse, inwhich one or more of the cells of the animal include a transgene. Atransgene is exogenous DNA containing a SNP of the present inventionwhich is integrated into the genome of a cell from which a transgenicanimal develops and which remains in the genome of the mature animal inone or more of its cell types or tissues. Such animals are useful forstudying the function of a variant protein in vivo, and identifying andevaluating modulators of variant protein activity. Other examples oftransgenic animals include, but are not limited to, non-human primates,sheep, dogs, cows, goats, chickens, and amphibians. Transgenic non-humanmammals such as cows and goats can be used to produce variant proteinswhich can be secreted in the animal's milk and then recovered.

A transgenic animal can be produced by introducing a SNP-containingnucleic acid molecule into the male pronuclei of a fertilized oocyte,e.g., by microinjection or retroviral infection, and allowing the oocyteto develop in a pseudopregnant female foster animal. Any nucleic acidmolecules that contain one or more SNPs of the present invention canpotentially be introduced as a transgene into the genome of a non-humananimal.

Any of the regulatory or other sequences useful in expression vectorscan form part of the transgenic sequence. This includes intronicsequences and polyadenylation signals, if not already included. Atissue-specific regulatory sequence(s) can be operably linked to thetransgene to direct expression of the variant protein in particularcells or tissues.

Methods for generating transgenic animals via embryo manipulation andmicroinjection, particularly animals such as mice, have becomeconventional in the art and are described in, for example, U.S. Pat.Nos. 4,736,866 and 4,870,009, both by Leder et al., U.S. Pat. No.4,873,191 by Wagner et al., and in Hogan, B., Manipulating the MouseEmbryo, (Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y.,1986). Similar methods are used for production of other transgenicanimals. A transgenic founder animal can be identified based upon thepresence of the transgene in its genome and/or expression of transgenicmRNA in tissues or cells of the animals. A transgenic founder animal canthen be used to breed additional animals carrying the transgene.Moreover, transgenic animals carrying a transgene can further be bred toother transgenic animals carrying other transgenes. A transgenic animalalso includes a non-human animal in which the entire animal or tissuesin the animal have been produced using the homologously recombinant hostcells described herein.

In another embodiment, transgenic non-human animals can be producedwhich contain selected systems that allow for regulated expression ofthe transgene. One example of such a system is the cre/loxP recombinasesystem of bacteriophage P1 (Lakso et al. PNAS 89:6232-6236 (1992)).Another example of a recombinase system is the FLP recombinase system ofS. cerevisiae (O'Gorman et al. Science 251:1351-1355 (1991)). If acre/loxP recombinase system is used to regulate expression of thetransgene, animals containing transgenes encoding both the Crerecombinase and a selected protein are generally needed. Such animalscan be provided through the construction of “double” transgenic animals,e.g., by mating two transgenic animals, one containing a transgeneencoding a selected variant protein and the other containing a transgeneencoding a recombinase.

Clones of the non-human transgenic animals described herein can also beproduced according to the methods described in, for example, Wilmut, I.et al. Nature 385:810-813 (1997) and PCT International Publication Nos.WO 97/07668 and WO 97/07669. In brief, a cell (e.g., a somatic cell)from the transgenic animal can be isolated and induced to exit thegrowth cycle and enter G_(o) phase. The quiescent cell can then befused, e.g., through the use of electrical pulses, to an enucleatedoocyte from an animal of the same species from which the quiescent cellis isolated. The reconstructed oocyte is then cultured such that itdevelops to morula or blastocyst and then transferred to pseudopregnantfemale foster animal. The offspring born of this female foster animalwill be a clone of the animal from which the cell (e.g., a somatic cell)is isolated.

Transgenic animals containing recombinant cells that express the variantproteins described herein are useful for conducting the assays describedherein in an in vivo context. Accordingly, the various physiologicalfactors that are present in vivo and that could influence ligand orsubstrate binding, variant protein activation, signal transduction, orother processes or interactions, may not be evident from in vitrocell-free or cell-based assays. Thus, non-human transgenic animals ofthe present invention may be used to assay in vivo variant proteinfunction as well as the activities of a therapeutic agent or compoundthat modulates variant protein function/activity or expression. Suchanimals are also suitable for assessing the effects of null mutations(i.e., mutations that substantially or completely eliminate one or morevariant protein functions).

For further information regarding transgenic animals, see Houdebine,“Antibody manufacture in transgenic animals and comparisons with othersystems”, Curr Opin Biotechnol. 2002 December; 13(6):625-9; Petters etal., “Transgenic animals as models for human disease”, Transgenic Res.2000; 9(4-5):347-51; discussion 345-6; Wolf et al., “Use of transgenicanimals in understanding molecular mechanisms of toxicity”, J PharmPharmacol. 1998 June; 50(6):567-74; Echelard, “Recombinant proteinproduction in transgenic animals”, Curr Opin Biotechnol. 1996 October;7(5):536-40; Houdebine, “Transgenic animal bioreactors”, Transgenic Res.2000; 9(4-5):305-20; Pirity et al., “Embryonic stem cells, creatingtransgenic animals”, Methods Cell Biol. 1998; 57:279-93; and Robl etal., “Artificial chromosome vectors and expression of complex proteinsin transgenic animals”, Theriogenology. 2003 Jan. 1; 59 (1):107-13.

EXAMPLES

The following examples are offered to illustrate, but not to limit theclaimed invention.

Example One Statistical Analysis of SNPs Associated with Psoriasis

Further information regarding Example One is disclosed in Cargill etal., “A large-scale genetic association study confirms IL12B and leadsto the identification of IL23R as psoriasis risk genes”, Am J Hum Genet.2007 February; 80(2):273-90, which is incorporated herein by reference.

Overview

A multi-tiered, case-control association study of psoriasis wasperformed in three independent, white North American sample sets (atotal of 1446 cases/1432 controls) with 26,644 gene-centric SNPs whichidentified an IL12B 3′UTR SNP (rs3212227) that was significantlyassociated with psoriasis in all three studies (OR_(common)=0.64,P_(comb)=7.85E-10). A Monte Carlo simulation to address multiple testingsuggests this association is not the result of a type I error. Thecoding regions of IL12B were resequenced in 96 individuals withpsoriasis and 30 additional IL12B-region SNPs genotyped. Haplotypes wereestimated and genotype-conditioned analyses were performed identifying asecond risk allele (rs6887695) located approximately 60 kb upstream ofthe IL12B-coding region that exhibited association with psoriasis afteradjusting for rs3212227. Together these two SNPs mark a commonIL12B-risk haplotype (OR_(common)=1.40, P_(comb)=8.11E-09) and a lessfrequent protective haplotype (OR_(common)=0.58, P_(comb)=5.65E-12) thatwere statistically significant in all three studies. Since IL12B encodesthe common IL-12p40 subunit of both IL-12 and IL-23, 17 SNPs in thegenes encoding the other chains of these two cytokines (IL12A and IL23A)and their receptors (IL12RB1, IL12RB2, IL23R) were individuallygenotyped. Haplotype analyses identified two IL23R missense SNPs thattogether mark a common psoriasis-associated haplotype in all threestudies (OR_(common)=1.44, P_(comb)=3.13E-06). Individuals homozygousfor both the IL12B and IL23R predisposing haplotypes appear to be atincreased risk for disease (OR_(common)=1.66, P_(comb)=1.33E-08). Thesedata, combined with the observation that administration of an antibodyspecific for the IL-12p40 subunit to psoriasis patients is highlyefficacious, suggest that these genes play a fundamental role inpsoriasis pathogenesis.

Introduction

A large, gene-centric association study focusing primarily on putativefunctional SNPs was carried out to identify psoriasis-susceptibilitymarkers in white North American case-control samples and to attempt toreplicate significant markers in a second independent case-controlsample set. During this process, significant association of a SNP(rs3212227) in the 3′UTR of IL12B with psoriasis was observed in bothcase-control sample sets.

To explore the role of IL12B in the genetics of psoriasis and identifypotentially causative variants, additional markers in and around IL12Bwere genotyped and further analyses were performed including haplotypeanalyses. Patients were stratified by clinically relevant variables andgenotypes to identify phenotype-specific or gene interaction effects.Because IL12B encodes the IL-12p40 subunit of two distinct heterodimericcytokines, IL-12 and IL-23¹⁴, SNPs in IL12A, IL23A (encoding theIL-12p35 and IL-23p19 subunits, respectively) and the genes encoding thereceptors of IL-12 and IL-23 (IL12RB1, IL12RB2, IL23R) were analyzed todetermine if polymorphisms within these IL12B-related genes also play arole in psoriasis susceptibility. Finally, all interesting findings werereplicated in a third independent white North American sample set.

Subjects and Methods Overall Strategy

Three sequential, case-control studies (discovery, replication 1 andreplication 2) were conducted to identify SNPs associated withpsoriasis. In the discovery study, DNA samples from individuals with(cases) and without (controls) psoriasis were genotyped for 26,644 SNPsusing disease-phenotype-based pooled DNAs to increase genotypingthrough-put and minimize DNA consumption. The allele frequency of eachSNP was determined as previously described¹⁵ and SNPs associated withpsoriasis at the 0.05 significance level were evaluated in anindependent sample set (replication study 1) by use of a similar poolingstrategy. Non-MHC-linked SNPs associated with psoriasis in thereplication study (P<0.05) with the same risk allele as in the discoverystudy were then individually genotyped in both discovery and replication1 sample sets to verify the results from the pooled DNA studies. SNPsthat were still significant (P<0.05) were then interrogated in a third,independent sample set (replication study 2). The results of those SNPswith P-values of less than 5.5E-05 from the pooled genotyping of thediscovery sample set are report herein in Example One.

Subjects

The discovery samples were collected from 467 white individuals withpsoriasis and 500 white individuals without psoriasis. The individualswith psoriasis were recruited from the University of Utah Department ofDermatology clinics as part of the Utah Psoriasis Initiative (UPI) andhad a diagnosis of psoriasis confirmed by a UPI investigator. A fullclinical work-up and questionnaire is available for each individual withpsoriasis and samples from first or second degree relatives were notincluded in the study. Samples from 500 white individuals matched forage and gender were selected from the Utah CEPH population and otherUniversity of Utah research studies to serve as controls. Fifty-threepercent of these individuals were without a reported history ofautoimmune disease. The remainder of the control individuals representthe general Utah population; no information on their autoimmune diseasestatus was available.

Replication sample set 1 was collected by the Genomics CollaborativeDivision of SeraCare Life Sciences (GCI; Cambridge, Mass.). Samples from498 white North American individuals with a confirmed medical diagnosisof psoriasis and a Psoriasis Area and Severity Index (PASI) score and498 white North American individuals without a history of psoriasis orother autoimmune disorders and individually matched to the cases bygender and age were included. Replication sample set 2, consisting ofsamples from 481 white North American individuals with psoriasis and 424white North Americans without psoriasis, was used to confirm resultsfrom the two previous sample sets; samples from 293 individuals withpsoriasis and 292 individuals without psoriasis were provided by GCI andmet the criteria described above. BioCollections Worldwide, Inc. (Miami,Fla.) provided an additional 188 samples from individuals with psoriasismeeting the same criteria described above as well as samples from 132white North Americans without psoriasis.

All individuals included in this study were over the age of 18 at thetime they were enrolled into the sample collections. In addition, noneof the subjects had undergone a bone marrow transplant. All protocolswere approved by national and/or local institutional review boards, andinformed written consent was obtained from all subjects. A detailedbreakdown of the clinical characteristics of the discovery andreplication sample set 1 is provided in Table 8. Detailed clinicalinformation for all individuals with psoriasis in replication sample set2 was limited. The female-male gender ratio was 0.89 and the average ageof onset was 28±16 years: 138 patients (28.7%) had disease onset at <18years of age, 180 patients (37.4%) between 18 and 32 years of age, and163 patients (33.9%) after 32 years of age.

Scan SNP Selection.

Allele-specific, kinetic PCR assays were developed for a collection of26,644 gene-centric SNPs curated from dbSNP, the Applera GenomeInitiative^(16,17) and the literature. SNPs were selected for inclusionif they appeared in more than one database and had a minor allelefrequency (MAF) of greater than or equal to 1%. Approximately 70% of theSNPs are missense polymorphisms predicted to modify the amino acidsequences encoded by the genes. The majority of the remaining 30% ofSNPs were splice site acceptor/donor SNPs, putative transcription factorbinding site SNPs, etc. Approximately 75% of the SNPs have MAFs greaterthan or equal to 5%.

Allele Frequency and Genotype Determination

SNP allele frequencies in pooled DNAs were determined byallele-specific, kinetic PCR as previously described.^(15,16) Briefly, 3ng of pooled DNA was amplified using allele-specific primers and allelefrequencies calculated from the two allele-specific PCR amplificationcurves, each determined in duplicate. Individual genotyping wasperformed using allele-specific kinetic PCR on 0.3 ng of DNA and thedata hand-curated prior to statistical analysis. Previous analysessuggest a genotyping accuracy of >99%.^(18,19) HLA-C genotypes weredetermined for the discovery samples by high-resolution sequence-basedtyping (performed by Atria Genetics, Inc.). HLA-C genotypes were notavailable for the other two sample sets.

Pools were constructed using an orthogonal design in which an individualDNA sample is arrayed into multiple pools based on the phenotypes(strata) to be studied (see Table 8 footnotes for a description of thepooling strategy for both the discovery and replication 1 sample sets).For the unstratified analyses (all cases vs. all controls) the allelefrequency measurements in each stratum were combined and averaged basedon the formula:

${freq} = {\frac{1}{2n}{\sum\limits_{j}\left\lbrack {\left( \frac{2}{l_{j}} \right){\sum\limits_{i}{f_{i}p_{ij}}}} \right\rbrack}}$

-   -   l_(j) is the number of pools that sample j appears in

$X_{ij} = \left\{ \begin{matrix}1 & {{if}\mspace{14mu} {sample}\mspace{14mu} j\mspace{14mu} {belongs}\mspace{14mu} {to}\mspace{14mu} {pool}\mspace{14mu} i} \\0 & {otherwise}\end{matrix} \right.$

-   -   f_(i)=allele frequency estimate in pool i    -   n is the number of distinct samples across all pools        By using an orthogonal pooling strategy, based on clinically        relevant phenotypes, and collapsing all the strata into an “all        cases versus all controls” analysis, repeated measurements were        essentially taken and therefore measurement error for the “all        vs. all” comparison was reduced while maintaining the ability to        examine associations with specific phenotypes.

IL12B Sequencing

To identify novel variants of IL12B, DNA from 96 randomly chosenindividuals with psoriasis in replication sample set 1 were selected forresequencing. Sequence data from all 8 annotated exons and 5000 bpupstream of the 5′-most exon of IL12B, which spans approximately 17 kb,were extracted from the R27 draft of the Celera human genome sequence(NCBI accession number NW_(—)922784). Primers were designed using thePrimer3 program²⁰ and included the M13 forward (5′ primer) or reverse(3′ primer) universal sequencing primer-binding sequence at their 5′ends. PCR amplification and sequencing were performed as previouslydescribed.¹⁹

SNP Selection for Follow-Up Genotyping

Tagging-SNPs (tSNPs) for follow-up genotyping were selected bydownloading the CEPH HapMap genotypes between positions 158,464,177 and158,828,966 on chromosome 5 from the HapMap web site (release #12,October 2004) and running the computer program Redigo.²¹ The approachused by this program maximizes the statistical power to detecttruly-associated SNPs while minimizing the number of SNPs that aregenotyped. For the power calculations, the following parameters wereset: 500 cases, 500 controls, 0.95 power threshold, disease prevalence0.01, and a conservative disease model [additive mode of inheritancewith genotypic relative risk (GRR)=1.5]. As described by Hu andcolleagues²¹, use of a conservative disease model produces a tSNP setthat is robust to changes in disease models.

LD Analysis and Haplotype Estimation

The LD measure r² was calculated from unphased data using the LDMAXprogram in the GOLD package.²² Spotfire software was used to generategraphical representations of the r² LD matrix. A pseudo-Gibbs samplingalgorithm from the program SNPAnalyzer version 1.0²³ was used toestimate haplotype and diplotype frequencies from unphased data. Casesand controls were treated separately in this process. The Haplo.Statspackage was used to test for association between haplotypes and diseasestatus.²⁴ The Haplo.Stats package is particularly useful on unphaseddata as it adjusts the variance on the test statistic for haplotypeestimation error.

Association Statistics

Allele counts in the pooled DNAs were estimated from theallele-specific, kinetic PCR amplification curves as previouslydescribed.¹⁵ A test of independence between allelic counts and diseasestatus was performed using Fisher's exact test.²⁵ For contingency tablesthe William's-corrected G test was applied to evaluate the nullhypothesis of independence between genotype counts and psoriasisstatus²⁶ except when the data were sparse. In that situation a MonteCarlo simulation approach was used to obtain appropriate P-values. Toexplore the extent of deviation from Hardy-Weinberg equilibrium, Weir'sexact test for Hardy-Weinberg equilibrium was calculated.²⁷ As manydisease models will predict a difference in pairwise linkagedisequilibrium between samples from affected individuals and samplesfrom unaffected individuals, the LD contrast test described by Nielsenand colleagues²⁸ was used to test for differences in linkagedisequilibrium patterns between cases and controls. Fisher's combinedP-value was employed to perform a joint analysis across independentstudies. A Mantel-Haenszel common odds ratio was calculated acrossindependent studies.²⁶ Genotypic relative risk estimates (γ) werecalculated using a Bayesian approach modeling (a) the prevalence ofdisease as a Beta distribution having an expected disease prevalence of2.3%, and (b) the genotype counts as being multinomially-distributedwith parameters estimated from the empirical data. The Breslow-Daystatistic²⁹ was used to determine whether SNPs exhibited significantheterogeneity of odds ratios across subphenotypes. A Cochran-Armitagetrend test³⁰ was used to test for trends in association withquantitative traits such as age of onset and disease severity. Estimatesof the population-attributable fraction were calculated as described byWalter³¹ and Schlesselman.³²

Since false positive results can be problematic in any large-scaleexperiment where modest nominal significance levels are used, a MonteCarlo simulation was performed to obtain the distribution of combinedP-values across the two studies. Two null hypotheses were considered andthe results presented under each hypothesis. Under the first model,which assumed independence between the cases and controls and a uniformdistribution of P-values, 26,000 stochastically-independent P-valueswere generated retaining the top 1,500 most significant. A second set of1,500 P-values were then generated under a uniform distribution tosimulate P-values for the replication data set and the two P-values foreach replicate combined using Fisher's combined P-value. The entire insilico experiment was repeated 1,000 times. The single most significantP-value from each in silico experiment was then used to generate adistribution of the thousand most-significant, experiment-wide P-values.This is analogous to results obtained under the Dunn-Sidak correctedsignificance level.²⁶ For the second model, a similar approach was usedexcept that the results from the pooled discovery samples were used tomodel the distribution of null p-values. This approach is typically moreconservative and is described in detail by Schrodi.³³ The resultinggamma null model was defined by scale and shape parameters of 1.2032 and0.8517, respectively. Similar to other empirical-based methods used onlarge-scale studies to obtain null or alternative models,³⁴ thisgamma-model method concurrently adjusts for diffuse effects ofpopulation stratification in a way similar to genomic controlapproaches.³⁵

Similar to the haplotype method,^(36,37) tests of association for aspecific SNP conditional on genotypes at other SNPs was performed. Thatis, cases and controls were both partitioned based on genotypes at oneSNP and the counts at an interrogated SNP were used in a test statistic.The statistical significance of the resulting test statistic wasassessed using a permutation procedure. This type of test is useful indissecting the relative contribution of each SNP to disease associationfor a set of SNPs in linkage disequilibrium.

To explore the likelihood of the data under various disease models,Bayes' factor curves^(38,39) were calculated using data from all threestudies. The results were compared against the likelihood of the dataunder a null hypothesis that accounts for diffuse populationstratification. Mathematically, the Bayes' factor can be written asB(R)=P[Data|H_(D)]/P[Data|H₀]; where R is the allelic relative risk,H_(D) is the disease model, a function of R, and H₀ is the nullhypothesis.³³

Results Discovery Scan and Replication Data

A gene-centric set of 26,644 SNPs was genotyped in pooled discoverysamples consisting of 466 white North American individuals withpsoriasis (cases) (DNA for one of the 467 patients was not available atthe time the pools were constructed) and 500 white North Americanindividuals without psoriasis (controls) from Utah (see Methods andTable 8). This set of SNPs was highly enriched for missense, nonsenseand regulatory polymorphisms.¹⁶ The discovery scan on DNA pools yielded32 markers with nominal p-values less than 5.5E-05, the majority ofwhich mapped within or near the MHC (Table 9). These 32 markers werethen genotyped in pooled replication 1 samples consisting of 498 whiteNorth American psoriasis cases and 498 white North American controlsmatched for age and gender. The majority of MHC-linked SNPs replicated(additionally, the frequency of the psoriasis-risk HLA allele, C*0602,was 22.4% in the discovery cases vs. 8.5% in the controls; OR=3.10, 95%CI 2.37-4.08, P=2.37E-17), however only one of the seven non-MHC-linkedmarkers, rs3212227, an A to C SNP in the 3′ untranslated region of IL12Blocated on 5q, replicated the discovery findings with a nominal p-valueof 1.88E-08 and consistent odds-ratio.

Individual genotyping of the IL12B 3′UTR marker, rs3212227, in thediscovery and replication 1 samples showed that Hardy Weinbergequilibrium could not be rejected for this SNP in the cases and controlsin both sample sets (P=0.847 in discovery cases and P=0.588 in discoverycontrols; P=0.297 in replication cases and P=0.913 in replicationcontrols) and confirmed association of the A (major) allele with riskfor psoriasis (discovery sample set: OR=1.59, P=1.89E-04; replicationsample set 1: OR=1.81, P=7.59E-07) with a Fisher's combined allelicP-value of 3.39E-09 and an OR_(common) of 1.70 (95% CI 1.43-2.01) (Table10). The common A allele is increased in cases compared to controls (86%vs. 78-79%). The association at rs3212227 appears to be independent ofHLA-C since stratification of the discovery patients and controls bypresence or absence of the HLA-C*0602 risk allele did not result insignificantly different odds ratios (HLA-C*0602 positive: OR=1.47,HLA-C*0602 negative: OR=1.61; Breslow-Day P-value=0.88).

Genotypic analysis of rs3212227 confirmed the single-marker results(Table 10). In a combined analysis of both sample sets, a single copy ofthe risk allele confers a modest risk (OR_(common)=1.47, 95% CI0.86-2.50), whereas individuals homozygous for the A allele were atgreater risk for psoriasis (OR_(common)=2.55, 95% CI 1.52-4.28).Bayesian estimates of relative risk, γ, for both the AA and CA genotypes(relative to the CC genotype) were also calculated in each sample setassuming disease prevalence was beta-distributed with a mean of 2.3%(Table 10). The resulting relative risk estimates, which are quitesimilar to the odds ratios, are also consistent with a general model ofinheritance in which individuals carrying one copy of the A allele atrs3212227 are at increased risk for psoriasis relative to non-carrierswhile individuals carrying two copies have an even greater risk.

Since false positive results can be problematic in any large-scaleexperiment, a Monte Carlo simulation was performed to obtain anexperiment-wide significance level. Two null hypotheses were consideredand results presented under each. Under the first null model, which issimilar to a Dunn-Sidak corrected significance level, independencebetween the cases and controls was assumed, generating a uniformdistribution of P-values (see the Methods). The resulting combinedP-values, which represent 1000 independent in silico experiments, rangedfrom 2.73E-04 to 2.58E-08 (Uniform-model. None of these are assignificant as the calculated Fisher's combined P-value of 3.39E-09 forthe IL12B 3′UTR SNP, rs3212227. The second model (Gamma-model), whichuses the results from the pooled discovery samples to model thedistribution of null P-values and is more conservative,³³ yielded 1,000null combined P-values ranging from 1.53E-04 to 2.30E-09. Only oneexceeded the significance of the Fisher's combined P-value of 3.39E-09for the IL12B 3′UTR SNP. Taken together, these data suggest thatassociation of the IL12B 3′ UTR SNP with psoriasis is unlikely to be theresult of a Type I error.

IL12B Sequencing in Patients with Psoriasis

In order to identify novel IL12B SNPs in patients with psoriasis, 1136bases of 5′ sequence, all exons and intron/exon boundaries, and 1424bases of 3′ sequence of IL12B were sequenced in 96 individuals withpsoriasis from replication sample set 1. On average, 89 individuals(coverage ranged from 70 to 96 individuals) were successfully sequencedand 6 SNPs were identified, one of which was not found in publicdatabases. The minor allele of this novel 3′UTR SNP (C158674941T;ss52085990) was observed on one chromosome in the 96 individualssequenced.

Single-Marker Analysis of the IL12B Region

To further explore the association signal in the IL12B region, acombination of tagging SNPs (tSNPs), which had the highest average powerto detect disease-associated variants under a conservative diseasemodel, and functional SNPs in the discovery and replication 1 samplesets were genotyped. 32 tSNPs from a total of 44 white Phase I HapMapSNPs were selected using the program Redigo²¹ in a region of 364.8 kbsurrounding IL12B on chromosome 5, 27 of which produced high qualityindividual genotyping data in both the discovery and replication samplesets with minimal loss in average power (0.89 for 27 tSNPs compared to0.90 for 32 tSNPs). A putative promoter polymorphism upstream of IL12B,ss52085993,^(41,42) which has been shown to affect IL-12 and IL-12 Bp40protein levels in stimulated cells, albeit inconsistently,⁴²⁻⁴⁵ as wellas two validated (SeattleSNPs resource) IL12B missense SNPs (rs3213119and rs3213096) (Table 11) were also individually genotyped for a totalof 30 additional SNPs in the IL12B region.

First, an exact test of Hardy-Weinberg Equilibrium (HWE) was used on thegenotypic data for these 30 variants, the analysis was performedseparately for cases and controls in each sample set (data not shown),and only two instances where a marker was not in HWE at the P<0.05significance level (rs7721001 in the discovery controls, P=0.005;rs270654 in the replication cases, P=0.020) were identified.

Next, all 30 variants were analyzed for single-marker association withpsoriasis susceptibility. In addition to the original hit, three otherSNPs, rs3212220, rs7709212, and rs6887695, all of which lie upstream ofthe IL12B ATG start codon, were associated at the 0.05 level in anallelic test with consistent odds ratios (i.e., the psoriasis riskallele was the same) in both sample sets (Table 11).

Haplotype Analysis of the IL12B-Region SNPs

To localize additional disease-associated variants, the linkagedisequilibrium patterns in this region were first examined bycalculating the pairwise r² values between all 31 markers separately forthe cases and controls in each study. The original hit, rs3212227, is inhigh LD with one of the tSNPs, rs3212220 (which lies in intron 1upstream of the IL12B ATG start codon) (r²>0.97 in the cases andcontrols of both sample sets), and in modest LD with rs1897565 (r²:0.25-0.33), ss52085993 (r²: 0.12-0.23), rs7709212 (r²: 0.28-0.40) andrs6887695 (r²: 0.15-0.31) but exhibits little to no LD with the 25 otherSNPs. Four other marker pairs show moderate LD (0.50<r²<0.80) in thecases and controls of both sample sets; however, the vast majority ofthe markers exhibited minimal LD between one another.

To focus on regions of interest, three marker, sliding window haplotypesfor the 28 markers with a minor allele frequency of greater than 5% weregenerated for the cases and controls from both sample sets using theHaplo.Stats program²⁴ and global P-values were calculated to assess theoverall haplotype frequency differences between the cases and controlsfor each 3-SNP window. Two peaks of replicated association with highlysignificant global P-values (P<0.0001) were observed. The first peak(w14-17) centers on the original marker, rs3212227, and rs3212220,which, as noted above, are in strong LD. The second peak (w22-24)centers on rs6887695. These three markers also showed the mostsignificant single-marker association (Table 11).

Based on these results, subsequent analyses focused on the 13 SNPs witha MAF>5% encompassed by these two peaks (rs11744690-rs4921226 in Table11). Detailed haplotype analyses of the SNPs in each peak as well asacross the entire 144 kb region (all 13 SNPs) using Haplo.Stats (datanot shown) suggested that 2 of these 13 SNPs, the original hit(rs3212227) and rs6887695, could explain the majority, if not all, ofthe association with psoriasis observed in both sample sets (Table 12).The 2-SNP haplotype containing the major allele of both SNPs(A@rs3212227-G@rs6887695) is significantly associated with risk in bothsample sets (discovery sample set: 71.4% in cases vs. 63.8% in controls,OR=1.42, P=3.27E-04; replication sample set 1: 71.9% in cases vs. 63.4%in controls, OR=1.48, P=1.35E-05) while a second haplotype containingthe minor allele at both SNPs (C—C) strongly confers protection(discovery sample set: 11% in cases vs. 16.6% in controls, OR=0.63,P=5.8E-04; replication sample set 1: 9.6% in cases vs. 19.0% incontrols, OR=0.46, P=2.56E-09).

Since the putative promoter variant, ss52085993, has been associatedwith risk of other inflammatory-based diseases,^(42,46) the haplotypeanalysis was specifically repeated including just ss52085993 along withrs3212227 and rs6887695. Alleles at ss52085993 do not appear to beresponsible for the haplotype association (Table 13). Each allele ispresent on both risk and protective haplotypes and the significance ofthe results, as measured by global P-values, was not improved byinclusion of this marker in the analysis. A similar analysis was donewith the two other significant SNPs in the single-marker analysis(rs3212220 and rs7709212 in Table 11). Inclusion of either or both alongwith rs3212227 and rs6887695 in the haplotype analysis did not increasethe significance of the results (data not shown), suggesting associationof both rs3212220 and rs7709212 with psoriasis in the single-markeranalysis is most likely explained by LD with either rs3212227, rs6887695or both.

Because the two implicated markers (rs3212227 and rs6887695) exhibitsome LD (r²: 0.15-0.31), a genotype-conditioned analyses similar to thatdescribed by Thomson and colleagues^(36,37) was carried out to see ifeither SNP was associated with disease independent of the other SNP. Todo this, a summary statistic was calculated for each sample set usingthe genotypes at one marker conditioned on the other marker and thesignificance of these observed results through a permutation test wasevaluated. The results of 20,000 permutations for each sample setdemonstrated that association of rs3212227 given rs6887695 (andrs6887695 given rs3212227) was significant at the 0.001 level in bothsample sets, indicating that each SNP contributes independently topsoriasis risk.

Replication of IL12B Results in a Third Sample Set & Combined Analyses

These results were then replicated in a third independent sample setconsisting of 481 white North American individuals with psoriasis and424 white North American controls by individually genotyping rs3212227and rs6887695. Allele frequencies were comparable across all threesample sets and single-marker analysis confirmed that both SNPs weresignificantly associated with psoriasis (rs3212227: P=0.014; rs6887695:P=0.007) (Table 11). The combined analysis for each individual SNPacross all three sample sets was highly significant (rs3212227:P_(comb)=7.85E-10; rs6887695: P_(comb)=4.08E-08). Haplotypes were alsoestimated in the third sample set confirming the common IL12B riskhaplotype (OR=1.31, P=6.00E-03) as well as the less frequent protectivehaplotype (OR=0.70, P=7.00E-03) (Table 12). Again, the combined analysesacross all three sample sets were highly significant (the frequency ofthe common risk A-G haplotype is approximately 71-72% in cases vs 64% incontrols, OR_(common)=1.40, 95% CI=1.25-1.57, P_(comb)=8.11E-09; thefrequency of protective C—C haplotype is 10-12% in cases vs 17-19% incontrols, OR_(common)=0.58, 95% CI=0.50-0.68, P_(comb)=5.65E-12) (Table12). Together these data provide convincing statistical evidence thatthe IL12B-region on 5q31.1-33.1 harbors a psoriasis susceptibilitylocus.

Analysis of IL12B Related Genes

IL12B encodes the common IL-12p40 subunit of two heterodimericcytokines, IL-12 and IL-23, each with a distinct subunit encoded by thegenes IL12A (IL-12p35) and IL23A (IL-23p19). The IL-12 and IL-23receptors also share a common subunit, encoded by IL12RB1, in additionto their unique components, IL23R for the IL-23 receptor and IL12RB2 forthe IL-12 receptor. To determine whether variants of these related genesmight be associated with psoriasis, the results of the 26,644 SNPgenome-wide scan were examined for markers in or near these 5 genes andassays were identified for 17 SNPs (7 in IL12A, 2 in IL23A, 4 inIL12RB1, 1 in IL12RB2 and 3 in IL23R) in the discovery sample set. Oneof these SNPs was significantly associated (P<0.05) with psoriasis inpooled DNAs from the discovery sample set, a missense SNP (L310P,rs7530511) in IL23R (data not shown).

To validate these results, all 17 SNPs were individually genotyped inthe discovery sample set, the association of rs7530511 with psoriasiswas confirmed (allele frequency 10.3% in cases, 14.6% in controls,OR=0.67, P=0.006), and a SNP in IL12A, rs2914119, was identified thatwas significant by individual genotyping (allele frequency 16% in cases,19.6% in controls, OR=0.78, P=0.045) (Table 14). In addition, haplotypeanalyses of the SNPs in each gene were carried out (data not shown).Since IL23R and IL12RB2 lie directly adjacent to one another onchromosome 1, all 4 SNPs in these 2 genes were used together in thehaplotype analysis of this region. A second IL23R SNP, rs11209026, wasfound to be associated with psoriasis risk in combination with rs7530511(Global P-value=0.004) (Table 15). The most common haplotype marked bythese two SNPs, C@rs7530511-G@rs11209026, was increased in patientscompared to controls (85.2% vs. 79.3%, OR=1.5, P=9.48E-04).

The two IL23R SNPs and the IL12A SNP were then individually genotyped inreplication sample set 1 (Table 14). The second IL23R SNP (rs11209026)was significantly associated with psoriasis in this sample set(P=0.004). Of most interest, however, was the observation that thecommon IL23R haplotype marked by the two SNPs rs7530511 and rs11209026(C-G) was also associated with psoriasis risk in replication sample set1 (haplotype frequency is 84.3% in cases vs. 79.7% in controls, OR=1.37,P=0.006) (Table 15). These findings were confirmed in the secondreplication sample set (Tables 14-15) and the combined analysis acrossall three sample sets was highly significant (frequency of the risk C-Ghaplotype was ˜85% in cases vs. 80% in controls, OR_(common)=1.44, 95%CI=1.25-1.65, P_(comb)=3.13E-06) providing strong statistical evidencefor a psoriasis-susceptibility gene on 1p31. It is interesting to notethat these two IL23R SNPs are both missense SNPs (rs7530511: L310P;rs11209026: Q381R) with an r² value between them of less than 0.01 inthe cases and controls of all three sample sets (data not shown). Thecommon risk haplotype carries a proline at amino acid 310 with anarginine at amino acid 381. Both of these amino acids are conserved inchimp, mouse, rat, cow, dog and chicken.

Additional Statistical Support of IL12B and IL23R Haplotypes

Complementing traditional homogeneity tests, significant differences inLD patterns between cases and controls can also indicate diseaseassociation. The test described in the Nielsen et al study²⁸ was appliedto pairs of SNPs in IL12B and IL23R. The results show departures betweencase and control pairwise values of D at rs3212227-rs6887695 for IL12Bas expected under a disease model: P_(cs-dis,ct-dis)=0.0129;P_(cs-rep1,ct-rep1)=5.16E-08; P_(cs-rep2,ct-rep2)=0.0497;P_(comb)=1.05E-08. With regard to rs7530511-rs11209026 at IL23R, the LDcontrast test yielded similar significant P-values:P_(cs-dis,ct-dis)=0.0085; P_(cs-rep1,ct-rep1)=0.0019;P_(cs-rep2,ct-rep2)=0.0019 P_(comb)=5.30E-06. As populationstratification and other sampling irregularities can generate departuresof pairwise LD measures from their null distribution, the LD contrasttest was performed on all combinations of control-control (i.e.,discovery controls vs replication 1 controls, discovery controls vsreplication 2 controls, etc.), case-case and case-control samples in anattempt to better understand the significance of the observed IL12B andIL23R results. In all instances for both genes, case-control comparisonswere statistically significant while the case-case and control-controlcomparisons were not, furthering support for the conclusions presentedhere (results not shown).

Bayes' factor curves were calculated for the risk rs3212227-rs6887695haplotype at IL12B and the risk rs7530511-rs11209026 haplotype IL23R.Bayes' factors were adjusted for population stratification and combinedacross the three independent studies.³³ These results show a maximumLog₁₀B(R) of 6.85 for the IL12B haplotype data, occurring at R=1.41.Hence, given equal prior probabilities for disease and null models, theprobability of the disease hypothesis given the data is approximately 7million-fold larger (at a disease model of R=1.41) than the probabilityof the null hypothesis conditioned on the data. Further, the Log₁₀B(R)is above 4 as R spans 1.15 to 1.73—testifying to the strength,robustness and specificity of the result. Similar results were observedfor the IL23R haplotype data with a maximum Log₁₀B(R) of 5.78 at R=1.47.Log₁₀B(R) remains above 4 for the relatively narrow range of1.20<R<1.80.

Diplotype Analyses

To better understand how both these genes influence risk of disease forindividuals, the impact of diplotypes at each locus independently andthen jointly on psoriasis risk was assessed. Of the ten possible 2-SNPIL12B diplotypes, nine were observed in the sample sets (Table 16). Thecombined results from all three sample sets suggest there is a hierarchyof risk depending on an individual's IL12B diplotype. Psoriasissusceptibility appears to be conferred by two copies of the common riskhaplotype (A-G/A-G: OR_(common)=1.52, 95% CI=1.31-1.77,P_(comb)=6.14E-07) which is found in approximately 48-51% of cases vs38-40% of controls. Individuals who carry a copy of both the riskhaplotype (A-G) and the protective haplotypes (A-G/C-C) appear to beprotected (OR_(common)=0.67, 95% CI=0.55-0.81, P_(comb)=1.39E-04).Although individuals homozygous for the protective haplotype (C-C/C-C)are relatively uncommon (˜1% of cases vs 2.3% of controls), the datapresented here suggest their risk of psoriasis may be even lower(OR_(common)=0.38, 95% CI 0.19-0.68, P_(comb)=0.018). Thepopulation-attributable fraction^(31,32) summarized across all threesample sets for the homozygous susceptible A-G/A-G IL12B diplotyperelative to all other diplotypes collapsed together was calculated to be17.1% [95% CI 11.4%-22.5%]).

At the IL23R locus, 7 of the 10 possible 2-SNP diplotypes were observed(Table 17). The data from all three sample sets suggest that individualshomozygous for the susceptible C-G haplotype are at increased risk forpsoriasis (OR_(common)=1.48, 95% CI=1.26-1.74, P_(comb)=2.24E-05) whileall other diplotypes appear to be neutral or protective. The estimatedpopulation-attributable fraction^(31,32) summarized across all threesample sets for the IL23R homozygous risk diplotype (C-G/C-G) relativeto all other diplotypes collapsed together is 23.5% (95% CI14.9%-31.5%).

To assess the joint impact of the IL12B and IL23R susceptiblehaplotypes, the distribution of diplotypes at both genes comparing casesand controls in all three sample sets was determined (Table 18). Giventhe large number of possible diplotypes across these two loci relativeto the number of cases and controls, the analysis concentrated on therisk haplotypes at each locus (A-G for IL12B and C-G for IL23R) and thethree non-risk haplotypes at each locus were grouped into a singlehaplotype (indicated as X for both loci in Table 18). The combinedanalysis shows that individuals homozygous for both the IL12B and IL23Rpredisposing haplotypes, approximately 35-37% of cases vs 24-27% ofcontrols, appear to be at increased risk for psoriasis(OR_(common)=1.66, 95% CI 1.41-1.95, P_(comb)=1.33E-08). A preliminarymeta-analysis of the available data suggests minimally a 3-folddifference in risk between individuals who carry one copy of theprotective C-C IL12B haplotype and one copy of the protective C-A IL23Rhaplotype (C-C/X-C-A/X) relative to individuals who are homozygous forrisk haplotypes at both loci (A-G/A-G-C-G/C-G) (data not shown).

Discussion

The identification of two psoriasis-susceptibility genes, IL12B andIL23R are disclosed in this Example. These genes were identified using acollection of over 26,000 primarily “functional” SNPs evaluated in threeindependent case-control sample sets (a total of 1446 patients and 1432controls) in a multi-staged strategy that combined pooled and individualgenotyping along with single-marker as well as multi-marker analyses.

As described in this Example, a second SNP upstream of IL12B, rs6887695,independently contributes to psoriasis risk and, together withrs3212227, these two SNPs mark a set of haplotypes having a hierarchy ofpsoriasis risk.

Regarding the effect of the 3′UTR rs3212227 SNP on IL-12 and IL-12p40expression levels, results using primary PBMCs suggest that IL12Btranscripts containing the A allele are expressed at a higher level thantranscripts containing the C allele when the RNAs from nine heterozygousdonors stimulated with LPS were measured using allele-specific,quantitative, reverse transcription-PCR; however, significant individualvariability in IL12B transcript levels was observed.

Both IL-12 and IL-23 have been proposed as targets for the treatment ofpsoriasis. IL-12p40 antagonists have been used for the effectivetreatment of a number of animal models of inflammatory-based diseasesincluding a mouse model of psoriasis⁶⁵ and monoclonal antibodiesdirected against the IL-12p40 subunit are now in clinical trials forCrohn's disease, multiple sclerosis, and psoriasis.⁶⁶⁻⁶⁸ Preliminaryresults show one of these biologics to be highly effective for psoriasistreatment.⁶⁷ However, given that IL12B encodes the common subunit of twofunctionally-distinct cytokines, IL-12 and IL-23,^(14,69) this antibodyeffectively functions as a broad-spectrum immune modulator, leading someto suggest that targeting only one of these cytokine pathways may be asafer yet equally effective therapy.⁷⁰

Biological data suggest the IL-23 pathway may be an important target forintervention in psoriasis. Psoriasis patients have significantlyincreased levels of IL23A and IL12B mRNA, but not IL12A mRNA, inpsoriatic lesions compared with non-lesional skin as determined byquantitative reverse transcription polymerase chain reaction.⁷¹ This hasrecently been confirmed by immunohistochemical staining.⁷² Moreoever, ina transgenic mouse model that overexpressed IL-12p40, IL-23 but notIL-12 was observed to be constitutively expressed by basal keratinocyteswhich are thought to play a pivotal role in psoriasis pathobiology.⁷³The findings presented in this Example that common variants in bothIL12B and IL23R are associated with psoriasis risk provides geneticevidence that the IL-23 pathway may be an appropriate target forintervention in psoriasis.

Thus, convincing statistical support is provided in this Example for twopsoriasis susceptibility loci—one in 5q31.1-q33.1 (IL12B-region) and theother in 1p31.3 (IL23R-region)- and analyses suggest the combination ofrisk and protective haplotypes at both loci can lead to more than a3-fold differential risk for disease. These data justify targeting boththe IL-12 and IL-23 pathways with new psoriasis therapeutics and suggestthat targeting IL-23 or downstream effector cytokines such as IL-17 maydirectly target the disease pathway and prove efficacious for thetreatment of psoriasis. Furthermore, the identified IL12B and IL23Rpsoriasis-associated alleles may be associated with response toanti-IL-12p40 therapy and/or the most effective dosage of this therapy.

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Example Two Statistical Analysis of IL12B SNPs Associated with Psoriasis

As described above in Example One, a multi-tiered, case-controlassociation study using a collection of over 25,000 gene-centric SNPs,identified association between a 3′UTR SNP in IL12B, rs3212227, andpsoriasis risk (allelic P_(comb)=7.85×10⁻¹⁰) in three independent samplesets (Cargill et al (2007) AJHG 80(2):273-90, incorporated herein byreference). To determine whether any other SNPs in this gene region wereassociated with psoriasis a combination of tag SNPs (N=27), which hadthe highest average power to detect disease-associated variants under aconservative disease model, and functional SNPs (N=3) was used togenotype the three sample sets. This identified a second risk allele,rs6887695 (allelic P_(comb)=4.08×10⁻⁸), located approximately 60 kbupstream of the IL12B coding region that exhibited association withpsoriasis after adjustment for rs3212227. Together these two SNPs markeda common IL12B risk haplotype (OR_(common) 1.40, P_(comb)=8.11×10⁻⁹) anda less frequent protective haplotype (OR_(common) 0.58,P_(comb)=5.65×10⁻¹²), which were statistically significant in all threestudies.

As a follow-up to the study described in Example One, 20 additional SNPswere selected for genotyping in the three psoriasis case-control samplesets and the data for all 51 IL12B-region SNPs is reported herein inExample Two.

All individuals in the sample sets included in this study were over theage of 18 at the time they were enrolled into the sample collections. Inaddition, none of the subjects had undergone a bone marrow transplant.All protocols were approved by national and/or local institutionalreview boards, and informed written consent was obtained from allsubjects. A detailed description can be found in Cargill et al (2007)AJHG 80(2):273-90.

SNP Selection

Twenty SNPs were selected for follow-up genotyping. All 20 of these SNPswere between positions (158537541-158812974)—covering 250 kbp—and werepreferentially selected to cover the IL12B coding region. These new SNPswere also chosen so that they (i) covered a range of r² values with thepreviously-described associated markers rs3212227 and rs6887695,concentrating on SNPs with higher levels of LD, (ii) were likely to bein evolutionarily-conserved regions (UCSC website) and (iii) hadfrequencies greater than 1% in whites of European descent. Lastly,redundancy within the new set of SNPs was reduced by eliminating someSNPs in extremely high LD (>0.97) with other SNPs selected.

Single Marker Analyses

The single marker results for all 51 SNPs are shown in Table 6,including the data for the 20 new SNPs. An exact test of HWE of thegenotype data for these 51 SNPs, with the analysis performed separatelyfor cases and controls in each sample set, identified only one instancewhere a marker was not in HWE at the P<0.01 significance level(rs7721001 in the discovery controls, P=0.005). Single marker analysesshowed that 9 of the 51 SNPs genotyped in this study in addition to thetwo original SNPs, rs3212227 and rs6887695, were associated withpsoriasis risk at P<10⁻⁶ (Table 6). The original 3′UTR SNP, rs3212227,however, remained the most significant marker across all three studieswith the minor C allele being highly protective (OR_(common) 0.64,P_(comb)=1.66E×10⁻⁹).

LD Patterns

The LD patterns in this region were next examined by calculating thepairwise r² values between all 51 markers. This was done separately forthe cases and controls in each sample set. Six of the eleven highlysignificant SNPs (p<10⁻⁶) (rs2082412, rs7730390, rs3213093, rs3212220,rs3212217 and the original IL12B 3′UTR SNP rs3212227), which are locatedwithin a 37 kb region that extends from intron 1 to the 3′ intergenicregion of IL12B, are highly correlated with each other in the cases andcontrols of all three sample sets (r²>0.97) (referred to as Group 1).Three of the eleven SNPs, rs6861600, rs7704367 and rs6887695 (the othersignificant independently associated SNP), which are located within a 3kb region approximately 60 kb 5′ of the IL12B gene, are highlycorrelated with each other in the cases and controls of all three samplesets (r²>0.98) (referred to as Group 2). The LD patterns of the tenthSNP, rs6894567, located in intron 1 of IL12B, show that it is reasonablycorrelated with rs3212227 (r²˜0.75-0.83) and modestly correlated withrs6887695 (r²-0.22-0.36). The eleventh significant SNP, rs7709212,located 5′ of IL12B and hypothesized to regulate IL12B expressionlevels, is modestly correlated with rs3212227 (r²˜0.28-0.40) andreasonably correlated with rs6887695 (r²˜0.70-0.79).

Conditional Analyses to Identify Independent Risk Alleles

To explore the possibility that SNPs aside from rs3212227 independentlyassociate with psoriasis, a logistic regression analysis was performedacross each of the three studies adjusting for rs3212227. P-values werecombined across the three independent studies using the Fisher'scombined P-value method and plotted by position. The combined analysispoints to a region 5′ to IL12B including SNPs rs6887695, rs1422878 andrs4921496 (P-value adjusted for rs3212227=0.028, P=0.0009, P=0.0013;respectively).

Conclusions

A thorough genetic interrogation and subsequent analysis of theIL12B-gene region reveals that the 3′UTR SNP rs3212227 is highlystatistically significantly associated with psoriasis.

A combined analysis across the three studies, adjusting for rs3212227,indicates that other SNPs 5′ of the coding region including rs6887695,rs1422878 and rs4921496, may contribute to psoriasis risk independentlyof rs3212227.

These data indicate that the set of SNPs in strong LD with rs3212227(Group 1) are primary drivers of the association at the IL12B-regionwith psoriasis.

Example Three Statistical Analysis of IL23R SNPs Associated withPsoriasis

As described above in Example One, a multi-tiered, case-controlassociation design, scanning over 25,000 gene-centric SNPs, identifiedtwo psoriasis susceptibility genes: IL12B and IL23R (Cargill et al(2007) AJHG 80(2):273-90, incorporated herein by reference). To refinethe initial association results and further investigate the regioninvolved in psoriasis risk, the genetic analysis was extended byresequencing the IL23R gene in 96 psoriatic individuals. Twelve novelIL23R SNPs were discovered including one missense, two synonymous andthree 3′ UTR SNPs. Using a series of selection criteria, 58 additionalIL23R-linked SNPs were identified which were genotyped in the threeindependent, white North American sample sets (>2800 individuals intow). Single marker and preliminary haplotype analyses have beenperformed on the full set of interrogated SNPs. A sliding window ofhaplotype association demonstrates co-localization of psoriasissusceptibility within the boundaries of IL23R across all sample sets,thereby decreasing the likelihood that neighboring genes, particularlyIL12RB2 which lies directly adjacent to IL23R, are driving theassociation of this region with psoriasis. The IL23R genetic variantsprovided herein may be associated with variability between differentindividuals in their response to psoriasis therapy and may be used todetermine the most effective dosage of therapy for a particularindividual.

SNP Selection

A multifaceted approach was carried out to identify SNPs to genotypeindividually in a fine-scale mapping effort in the IL23R region. A 336kbp region was selected across a portion of Clorf141 through SERBP1.SNPs in moderate to high LD (r²>0.20) with the two originally-identifiedmissense SNPs, rs7530511 and rs11209026, were initially selected andreduced so that those pairs within this group in extremely high LD(r²>0.97) were represented by one or two SNPs. Next, the tagging SNPprogram Redigo (Hu et al (2004) Hum Hered 57:156-170) was run to selecta set of tagging SNPs among those in weak LD (r²<0.20) with the originaltwo SNPs. Further, any SNPs with putative function annotation wereselected to be genotyped. In all, 61 SNPs were identified to cover theIL23R region.

Single SNP Analyses

The allelic association P-values were plotted as a function of positionfor the 61 SNPs selected to interrogate the IL23R region. The peak ofpsoriasis association occurs over the IL23R coding region.

Genotype Analyses

Six individual SNPs with combined P-values below 0.05 are presented indetail showing genotype frequencies in cases and controls across thethree sample sets (Table 19). Notably, the Q381R SNP (rs11209026)yielded the most significant combined association results.

Linkage Disequilibrium

Pairwise linkage disequilibrium was calculated across cases and controlscombined as an exploratory analysis to better understand the detailedstructure in the IL23R region (Abecasis et al (2000) Bioinformatics16:182-183). The correlation statistic r² was employed. As much of theassociation signal was being driven by rs11209026, the decay ofrs11209026-LD was elucidated in a positional fashion. A single SNPattained a r² value greater than 0.20 in the sample sets: rs11465804,located in an IL23R intron. A genotypic-based squared correlationcoefficient was calculated between the two sites: Discovery casesg²=0.902, Discovery controls g²=0.888, Repl 1 cases g²=0.705, Repl 1controls g²=0.870, Repl 2 cases g²=0.914, and Repl 2 controls g²=0.872.Two-SNP analyses of rs11209026 and rs11465804 indicate that recombinantsbetween the two SNPs are reasonably rare in the samples.

Haplotype Analyses

Using a sliding window of haplotype association on 3 adjacent SNPs, twoneighboring windows composed of rs10789229, rs10889671, rs11209026 andrs10889674 were identified that yielded the peak global association withpsoriasis when analyses are combined across the three independent samplesets (window 1 combined P-value=1.28E-04 and window 2 combinedP-value=6.42E-05). Both windows were significant (P<0.05) in each of thethree studies. These four SNPs reside within a 12 kbp region withinIL23R. Table 21 provides IL23R region sliding window haplotypeassociation 3-SNP windows.

SNPs significantly associated using a Fisher's combined P-value acrossall three studies (6

SNPs from Table 19) were examined and partitioned into three groups onthe basis of LD structure and significance. Group1: rs7530511,rs10889671, rs1857292; Group2: rs11465804 and rs11209026; Group3:rs10889674. One representative SNP from each group was used in ahaplotype analysis where phase was estimated through a pseudo-Gibbssampling algorithm (SNPAnalyzer program). See Yoo et al (2005) NucleicAcids Res 33:W483-W488. Haplotypes segregating atrs7530511-rs11209026-rs10889674 were then evaluated for statisticalassociation. One psoriasis-predisposing haplotype (C-G-G), one neutralhaplotype (C-G-T) and two protective haplotypes (T-G-T and C-A-T) werefound (Table 20).

Conclusions

Fine-scale mapping of the IL23R region across three independentpsoriasis case-control sample sets shows variants segregating at IL23Rthat are significantly associated with disease.

As with several other inflammation disease susceptibility genesexhibiting pleiotropy, variants at IL23R now appear to conferrisk/protection for psoriasis and IBD. A novel SNP-selection procedureto fine-scale map regions with initial association data is discussedherein. Single marker analysis produces little evidence that theIL23R-neighboring gene IL12RB2 (also a functional candidate) isgenerating the patterns of psoriasis-association in the region.Three-SNP haplotype sliding window analysis yields a peak association of6.42E-05 located at IL23R with the adjacent SNPsrs10889671-rs11209026-rs10889674. Additional haplotype analyses withrepresentative SNPs derived from LD groups, demonstrate that haplotypessegregating at rs7530511-rs11209026-rs10889674 (L310P-Q381R-putativetranscription factor binding site) generate susceptible, neutral andprotective effects with regard to psoriasis.

Example Four Additional LD SNPs Associated with Psoriasis

Another investigation was conducted to identify SNPs in linkagedisequilibrium (LD) with certain “interrogated SNPs” which have beenfound to be associated with psoriasis, as shown in the tables. Theinterrogated SNPs, which are shown in column 1 (which indicates the hCVidentification numbers of each interrogated SNP) and column 2 (whichindicates the public rs identification numbers of each interrogated SNP)of Table 4. The methodology is described earlier in the instantapplication. To summarize briefly, the power threshold (T) was set at anappropriate level, such as 51%, for detecting disease association usingLD markers. This power threshold is based on equation (31) above, whichincorporates allele frequency data from previous disease associationstudies, the predicted error rate for not detecting trulydisease-associated markers, and a significance level of 0.05. Using thispower calculation and the sample size, for each interrogated SNP athreshold level of LD, or r² value, was derived (r_(T) ², equations (32)and (33) above). The threshold value r_(T) ² is the minimum value oflinkage disequilibrium between the interrogated SNP and its LD SNPspossible such that the non-interrogated SNP still retains a powergreater or equal to T for detecting disease-association.

Based on the above methodology, LD SNPs were found for the interrogatedSNPs. Several exemplary LD SNPs for the interrogated SNPs are listed inTable 4; each LD SNP is associated with its respective interrogated SNP.Also shown are the public SNP IDs (rs numbers) for the interrogated andLD SNPs, when available, and the threshold r² value and the power usedto determine this, and the r² value of linkage disequilibrium betweenthe interrogated SNP and its matching LD SNP. As an example in Table 4,the interrogated, psoriasis-associated SNP rs6887695 (hCV1994992) wascalculated to be in LD with rs11135059 (hCV11269323) at an r² value of0.96248, based on a 51% power calculation, thus establishing the latteras a marker associated with psoriasis as well.

All publications and patents cited in this specification are hereinincorporated by reference in their entirety. Various modifications andvariations of the described compositions, methods and systems of theinvention will be apparent to those skilled in the art without departingfrom the scope and spirit of the invention. Although the invention hasbeen described in connection with specific preferred embodiments andcertain working examples, it should be understood that the invention asclaimed should not be unduly limited to such specific embodiments.Indeed, various modifications of the above-described modes for carryingout the invention that are obvious to those skilled in the field ofmolecular biology, genetics and related fields are intended to be withinthe scope of the following claims.

TABLE 1 Transcript SNP info and associated gene/protein informationGene Number: 1 Celera Gene: hCG16056 - 84000313410292 Celera Transcript:hCT7086 - 84000313410293 Public Transcript Accession: NM_002187Celera Protein: hCP35440 - 197000069447233 Public Protein Accession:NP_002178 Gene Symbol: IL12B Protein Name:interleukin 12B (natural killer cell stimulatoryfactor 2, cytotoxic lymphocyte maturation factor 2, p40)Celera Genomic Axis: GA_x5YUV32VUFE (16577243..16609245) Chromosome: 5OMIM NUMBER: 161561 OMIM Information:BCG and salmonella infection, disseminated, 209950 (1);{Asthma,/suscepti- bility to}, 600807 (3)Transcript Sequence (SEQ ID NO: 1): Protein Sequence (SEQ ID NO: 4):SNP Information Context (SEQ ID NO: 7):AAGACACAACGGAATAGACCCAAAAAGATAATTTCTATCTGATTTGCTTTAAAACGTTTTTTTAGGATCACAATGATATCTTTGCTGTATTTGTATAGTT MGATGCTAAATGCTCATTGAAACAATCAGCTAATTTATGTATAGATTTTCCAGCTCTCAAGTTGCCATGGGCCTTCATGCTATTTAAATATTTAAGTAATT Celera SNP ID: hCV2084293Public SNP ID: rs3212227 SNP in Transcript Sequence SEQ ID NO: 1SNP Position Transcript: 1148 SNP Source: dbSNP; Celera; HapMap; HGBASEPopulation (Allele,Count): caucasian (A,93|C,27) SNP Type: UTR3Context (SEQ ID NO: 8):TGTCTGGAAGGCAAAAAGATCTTAAGATTCAAGAGAGAGGACAAGTAGTTATGGCTAAGGACATGAAATTGTCAGAATGGCAGGTGGCTTCTTAACAGCC MTGTGAGAAGCAGACAGATGCAAAGAAAATCTGGAATCCCTTTCTCATTAGCATGAATGAACCTGATACACAATTATGACCAGAAAATATGGCTCCATGAA Celera SNP ID: hCV7537839Public SNP ID: rs1368439 SNP in Transcript Sequence SEQ ID NO: 1SNP Position Transcript: 2084 SNP Source: dbSNP; Celera; HapMap; HGBASE;Population (Allele,Count): caucasian (C,26|A,94) SNP Type: UTR3Gene Number: 2 Celera Gene: hCG1807923 - 30000023016764Celera Transcript: hCT1847183 - 30000023016767Public Transcript Accession: Celera Protein: hCP1738390 - 30000023016751Public Protein Accession: Gene Symbol: IL23R Protein Name:interleukin-23 receptor Celera Genomic Axis:GA_x5YUV32W802 (15859517..15972828) Chromosome: 1 OMIM NUMBER: 607562OMIM Information: Transcript Sequence (SEQ ID NO: 2):Protein Sequence (SEQ ID NO: 5): SNP Information Context (SEQ ID NO: 9):TCTGACAACAGAGGAGACATTGGACTTTTATTGGGAATGATCGTCTTTGCTGTTATGTTGTCAATTCTTTCTTTGATTGGGATATTTAACAGATCATTCC RAACTGGGATTAAAAGAAGGATCTTATTGTTAATACCAAAGTGGCTTTATGAAGATATTCCTAATATGAAAAACAGCAATGTTGTGAAAATGCTACAGGAA Celera SNP ID: hCV1272298Public SNP ID: rs11209026 SNP in Transcript Sequence SEQ ID NO: 2SNP Position Transcript: 428 SNP Source: dbSNP; Celera; HapMapPopulation (Allele,Count): caucasian (G,112|A,8) SNP Type:Missense Mutation Protein Coding:SEQ ID NO: 5, at position 143,(R,CGA) (Q,CAA) Context (SEQ ID NO: 10):GTGCAACAGTCAGAATTCTACTTGGAGCCAAACATTAAGTACGTATTTCAAGTGAGATGTCAAGAAACAGGCAAAAGGTACTGGCAGCCTTGGAGTTCAC YGTTTTTTCATAAAACACCTGAAACAGTTCCCCAGGTCACATCAAAAGCATTCCAACATGACACATGGAATTCTGGGCTAACAGTTGCTTCCATCTCTACA Celera SNP ID: hCV2990018Public SNP ID: rs7530511 SNP in Transcript Sequence SEQ ID NO: 2SNP Position Transcript: 215 SNP Source: dbSNP; Celera; HapMapPopulation (Allele,Count): caucasian (T,15|C,105) SNP Type:Missense Mutation Protein Coding:SEQ ID NO: 5, at position 72,(P,CCG) (L,CTG) Gene Number: 2 Celera Gene:hCG1807923 - 30000023016764 Celera Transcript:hCT2349254 - 30000023016772 Public Transcript Accession: NM_ 144701Celera Protein: hCP1914504 - 30000023016752 Public Protein Accession:NP_653302 Gene Symbol: IL23R Protein Name: interleukin-23 receptorCelera Genomic Axis: GA_x5YUV32W802 (15859517..15972828) Chromosome: 1OMIM NUMBER: 607562 OMIM Information:Transcript Sequence (SEQ ID NO: 3): Protein Sequence (SEQ ID NO: 6):SNP Information Context (SEQ ID NO: 11):TCTGACAACAGAGGAGACATTGGACTTTTATTGGGAATGATCGTCTTTGCTGTTATGTTGTCAATTCTTTCTTTGATTGGGATATTTAACAGATCATTCC RAACTGGGATTAAAAGAAGGATCTTATTGTTAATACCAAAGTGGCTTTATGAAGATATTCCTAATATGAAAAACAGCAATGTTGTGAAAATGCTACAGGAA Celera SNP ID: hCV1272298Public SNP ID: rs11209026 SNP in Transcript Sequence SEQ ID NO: 3SNP Position Transcript: 1227 SNP Source: dbSNP; Celera; HapMapPopulation (Allele,Count): caucasian (G,112|A,8) SNP Type:Missense Mutation Protein Coding:SEQ ID NO: 6, at position 381,(R,CGA) (Q,CAA) Context (SEQ ID NO: 12):GTGCAACAGTCAGAATTCTACTTGGAGCCAAACATTAAGTACGTATTTCAAGTGAGATGTCAAGAAACAGGCAAAAGGTACTGGCAGCCTTGGAGTTCAC YGTTTTTTCATAAAACACCTGAAACAGTTCCCCAGGTCACATCAAAAGCATTCCAACATGACACATGGAATTCTGGGCTAACAGTTGCTTCCATCTCTACA Celera SNP ID: hCV2990018Public SNP ID: rs7530511 SNP in Transcript Sequence SEQ ID NO: 3SNP Position Transcript: 1014 SNP Source: dbSNP; Celera; HapMapPopulation (Allele,Count): caucasian (T,15|C,105) SNP Type:Missense Mutation Protein Coding:SEQ ID NO: 6, at position 310,(P,CCG) (L,CTG)

TABLE 2 Genomic SNP info and associated gene information Gene Number: 1Celera Gene: hCG16056 - 84000313410292 Gene Symbol: IL12B Protein Name:interleukin 12B (natural killer cell stimulatory factor 2,cytotoxic lymphocyte maturation factor 2, p40) Celera Genomic Axis:GA_x5YUV32VUFE (16577243..16609245) Chromosome: 5 OMIM NUMBER: 161561OMIM Information:BCG and salmonella infection, disseminated, 209950 (1);{Asthma,/suscepti- bility to}, 600807 (3)Genomic Sequence (SEQ ID NO: 13): SNP InformationContext (SEQ ID NO: 24):CTGTGTGCCCAGCACTTCCTCTGCATGCCTCAGATGCATTTGACAATCTCAGGTGAACTGCACTTCAGGGTCAAGGGAACCCCGGCCATGGTTCTAAGAA RCAACTCCCATTTTAGTATCACCTACATTTGAAACCACAGAGCACTGTCCAGGAGAGGTGATGGTGGTGGGTCTCCTCCTTTGGCTCTCTGGCCCATCAGC Celera SNP ID: hCV1992693Public SNP ID: rs1433048 SNP in Genomic Sequence: SEQ ID NO: 13SNP Position Genomic: 24055 SNP Source:dbSNP; Celera; HapMap; ABI_Val; HGBASE Population (Allele,Count):caucasian (G,21|A,99) SNP Type: INTRON Context (SEQ ID NO: 25):AATTACTTAAATATTTAAATAGCATGAAGGCCCATGGCAACTTGAGAGCTGGAAAATCTATACATAAATTAGCTGATTGTTTCAATGAGCATTTAGCATC KAACTATACAAATACAGCAAAGATATCATTGTGATCCTAAAAAAACGTTTTAAAGCAAATCAGATAGAAATTATCTTTTTGGGTCTATTCCGTTGTGTCTT Celera SNP ID: hCV2084293Public SNP ID: rs3212227 SNP in Genomic Sequence: SEQ ID NO: 13SNP Position Genomic: 11160 SNP Source: dbSNP; Celera; HapMap; HGBASEPopulation (Allele,Count): caucasian (T,93|G,27) SNP Type: UTR3; INTRONContext (SEQ ID NO: 26):GGCTTTGTCCAGTGATTTTAAAAGTGGGGTGAAAGGAGTCTGGGGCGGTACAAAAGGGCCTCTGGAACCTTGCAACAGGCAAAGGAATTCTGCTGTAAGG YGAGGAAGCTGGGAAGCCAATATCTTAGCCTCTATAAGTGTAGACATTCTGTTTAGTAAAATAATTTTATAATATCTGGAACAGCCAGGAGCTATCCATTT Celera SNP ID: hCV2084296Public SNP ID: rs2853696 SNP in Genomic Sequence: SEQ ID NO: 13SNP Position Genomic: 12870 SNP Source:dbSNP; Celera; HapMap; ABI_Val; HGBASE Population (Allele,Count):caucasian (T,26|C,94) SNP Type: INTRON Context (SEQ ID NO: 27):CCCCTCTGACTCTCTCTGCAGAGAGTGTAGCAGCTCCGCACGTCACCCCTTGGGGGTCAGAAGAGCTGAAGTCAAAGACAGAAATTAGCCTGTGTTACAC MTTGGGGAGAGAGTTCCTAGTGATTGTAGCCAGTAAGGCAGGTAAGGCCTCAACTGTTGTCTGAGGACACAGTTTCTCCAACTGGGCTGATTTCTACCCAG Celera SNP ID: hCV2084297Public SNP ID: rs919766 SNP in Genomic Sequence: SEQ ID NO: 13SNP Position Genomic: 15774 SNP Source: dbSNP; Celera; HapMap; HGBASEPopulation (Allele,Count): caucasian (A,110|C,10) SNP Type: INTRONContext (SEQ ID NO: 28):GTCTGCTTCAGGGCCCCTAAGATCTACGCCCTGGAGCTCTTGTTTTTATTTTTGACTCAAGGTGCAATTTCAGCAAGTCATTTGTAGCTTTGAATTCTCC KTTTATCCCTTTCTTTGGTGCTATGAGGCTTCAGGAAGCATGGCCAGGCAATTTGGATGAGTGGGTTCAAACACAGCAGAGACTATTCTCAGTTCCCAATA Celera SNP ID: hCV2084298Public SNP ID: rs2853694 SNP in Genomic Sequence: SEQ ID NO: 13SNP Position Genomic: 17298 SNP Source: dbSNP; Celera; HapMap; HGBASEPopulation (Allele,Count): caucasian (G,65|T,55) SNP Type: INTRONContext (SEQ ID NO: 29):TATCTGCCTTACATTTGACTGAGGATTAAATGAAAAAAAAAAAAAGCACGTAAAGTACTTAGCACAGTGTCTGCCACACAGTAAATTCGGTGTTAGTTAT YGTTACTTATAGACTGAGGAGTCAGCCAACTGTACAGAGAAACTCTCTTAACAATTTTCCATGGATATTTAAGGATTTCGTTCCCTCTGTTTTAAATCACC Celera SNP ID: hCV2084301Public SNP ID: rs3213093 SNP in Genomic Sequence: SEQ ID NO: 13SNP Position Genomic: 19189 SNP Source: dbSNP; Celera; HGBASEPopulation (Allele,Count): caucasian (C,93|T,27) SNP Type:INTRON; REPEATS Context (SEQ ID NO: 30):TTCATGGAGCCATATTTTCTGGTCATAATTGTGTATCAGGTTCATTCATGCTAATGAGAAAGGGATTCCAGATTTTCTTTGCATCTGTCTGCTTCTCACA KGGCTGTTAAGAAGCCACCTGCCATTCTGACAATTTCATGTCCTTAGCCATAACTACTTGTCCTCTCTCTTGAATCTTAAGATCTTTTTGCCTTCCAGACA Celera SNP ID: hCV7537839Public SNP ID: rs1368439 SNP in Genomic Sequence: SEQ ID NO: 13SNP Position Genomic: 10224 SNP Source: dbSNP; Celera; HapMap; HGBASE;Population (Allele,Count): caucasian (G,26|T,94) SNP Type: UTR3; INTRONContext (SEQ ID NO: 31):GCAATGCTCAACTGTTTCAGTCAAATACCTTAAAAATGAGCATTCCTGGGTTGGGTGACGGAATATTGACAAATTACAGCTTTGTCAGAACTGCTACTAA STCTAGGCGGACCTTGCTATGTACTTTATTCCCTTATAAAGTTTGTGAGTGGCAGAGACAGGCCTAGAAGTCAAGCCTTCTTGGACACTGCTCAGTGCTGT Celera SNP ID: hCV27471935Public SNP ID: rs3212217 SNP in Genomic Sequence: SEQ ID NO: 13SNP Position Genomic: 23340 SNP Source: dbSNP; HapMap; HGBASEPopulation (Allele,Count): caucasian (G,93|C,27) SNP Type: INTRONContext (SEQ ID NO: 32):GCCTCTCTGAGGAGGTGACATTTAGCTGACACCAAAAGGAAAGATGTCAGTTGTGTTAAGAGCAGAGGGAAGCATATGTGCGAAGCACCTGCTAGGAGCC RTGATCTTTGTGTGGAGCAGTGCCAGGCCTACAGAGCCCAACCACACACCCTAGCATGTCTCTGCCTCCTCTTATCTAGAAGACCTAATTGAGGAAGGAGT Celera SNP ID: hCV32389158Public SNP ID: rs3213102 SNP in Genomic Sequence: SEQ ID NO: 13SNP Position Genomic: 14806 SNP Source: dbSNP; HapMap; HGBASEPopulation (Allele,Count): caucasian (G,116|A,2) SNP Type: INTRONContext (SEQ ID NO: 33):CTCTTATTTTTAAGATGAGAAACTTAAAGCTTAGAGAAGGAATGTGACTTTCTGGATCAACATCTAGCAGTTGTTTATTTAGTGCTTACTACATAAAGAG MACTGGGCTAGAAGCAGTTGAGAGAGAAAAAAAGGGCTTACCTGGATCCCGCTTCCTAGGAGCAAATACTTTTACTCAATAAATATTTATTAAGTCAGTGT Celera SNP ID: hDV68553886Public SNP ID: rs3212220 SNP in Genomic Sequence: SEQ ID NO: 13SNP Position Genomic: 22405 SNP Source: dbSNP; HapMap; ABI Val; HGBASEPopulation (Allele,Count): caucasian (C,93|A,27) SNP Type: INTRONContext (SEQ ID NO: 34):AGATTGTGATTCAGATCTGGGATGGGGCTCAGGAACCTGCATTTTAACAATGGAGGTTCTAATGTGGTCATTGGCAGGTTGTTCTAATGTGGGGGCCACA GCCTCTCTCGGAGACAGGCTGTACATGGCCAGCCAGCATTCTGGTAATATGAGCCAAATGCCCATTGACCTAATTTTGGAGAAGAGGTTTATCAACATGTC Celera SNP ID: hDV70663683Public SNP ID: rs17860508 SNP in Genomic Sequence: SEQ ID NO: 13SNP Position Genomic: 309920 SNP Source: CDX Population (Allele,Count):no_pop (G,26|T,94) SNP Type: INDEL; PROMOTER Context (SEQ ID NO: 35):ATAGCTTTTCATTTTTTAACTGGGGCCAAAGTTAGTTAATCCACAAGAATGGGGATCCCAGCTGTCATTTTGGTTGATATCACAACTGACGACCAAGACC RTCACAAATATGGGAGCAAGTCTGATTTGTAACATTATTATAATTATGAATCCAATTACTTTAAGGAATGCACGAAAGGCTTTTTAAAAATTTCAATAGTA Celera SNP ID: hDV71045748Public SNP ID: rs6894567 SNP in Genomic Sequence: SEQ ID NO: 13SNP Position Genomic: 25178 SNP Source: dbSNP; HapMapPopulation (Allele,Count): caucasian (A,94|G,26) SNP Type: INTRONContext (SEQ ID NO: 36):GCTTCCCAACACTGGTGCCAACATCAGCATGCCAATGACGCTTGGGTGGGCATCTCTTTTTTAAGTTTTGTTTGTTACCTTTAATTAGAAAAGGGAGTTA YTATTAAAGAATAAAAAGATATAGTCACAGATGGCATGGGTGGCACATGCAAAGATCCCTAGTTTGAGGGCCAGGCAGACTTCTAGAACTGTGTGACTCTG Celera SNP ID: hCV3169809Public SNP ID: rs730691 SNP in Genomic Sequence: SEQ ID NO: 13SNP Position Genomic: 24437 Related Interrogated SNP:hCV2084293 (Power = .6) Related Interrogated SNP: hCV30449508 (Power =.6) SNP Source: dbSNP; Celera; HapMap; ABI_Val; HGBASEPopulation (Allele,Count): caucasian (T,47|C,73) SNP Type:TRANSCRIPTION FACTOR BINDING SITE; INTRON Context (SEQ ID NO: 37):GAGAAACTTCCAGCACAATTTCAGTTTCATAGAGAATACGGCAGGGCACAATATTCAGCAGAGTAACATAGTGGTTAAAAGCTCAGGGTGTCGAGAACAA YGAACCAAGACTGTCATCCTGTCTCCACTAACCAGCTGGGGGATTTGGAACAAGGTATTTCATTATCATGAGCCTCAGTTTCCTCATCTGTAAAATGATAA Celera SNP ID: hCV29927086Public SNP ID: rs3213094 SNP in Genomic Sequence: SEQ ID NO: 13SNP Position Genomic: 18979 Related Interrogated SNP:hCV2084293 (Power = .9) Related Interrogated SNP: hCV30449508 (Power =.8) SNP Source: dbSNP; HapMap; HGBASE Population (Allele,Count):caucasian (C,93|T,27) SNP Type: INTRON; REPEATS Gene Number: 2Celera Gene: hCG1807923 - 30000023016764 Gene Symbol: IL23RProtein Name: interleukin-23 receptor Celera Genomic Axis:GA x5YUV32W802 (15859517..15972828) Chromosome: 1 OMIM NUMBER: 607562OMIM Information: Genomic Sequence (SEQ ID NO: 14): SNP InformationContext (SEQ ID NO: 38):TTAGACAACAGAGGAGACATTGGACTTTTATTGGGAATGATCGTCTTTGCTGTTATGTTGTCAATTCTTTCTTTGATTGGGATATTTAACAGATCATTCC RAACTGGGTAGGTTTTTGCAGAATTTCTGTTTTCTGATTTAGACTACATGTATATGTATCACCAAAATTTAGTCATTTCAGTTGTTTACTAGAAAAATCTG Celera SNP ID: hCV1272298Public SNP ID: rs11209026 SNP in Genomic Sequence: SEQ ID NO: 14SNP Position Genomic: 83622 SNP Source: dbSNP; Celera; HapMapPopulation (Allele,Count): caucasian (G,112|A,8) SNP Type:MISSENSE MUTATION; HUMAN-MOUSE SYNTENIC REGION Context (SEQ ID NO: 39):AACCCGGTACCTCAGTTGGAAATGAAGAAATCGTTCGTCTTCTGCATCATTCACGCTGGGAGCTGTAGACTGGAGCTGTTCCTATTCAGCCATCTTGGCT YGGGACCAGAGAACTTCGTATTTCTTACAGCACCTCCTAAGTGTTATGTTTTGTTGCAGATCCGCCAGATATTCCTGATGAAGTAACCTGTGTCATTTATG Celera SNP ID: hCV1272348Public SNP ID: rs6687620 SNP in Genomic Sequence: SEQ ID NO: 14SNP Position Genomic: 26292 SNP Source: dbSNP; Celera; HapMapPopulation (Allele,Count): caucasian (T,14|C,104) SNP Type:TRANSCRIPTION FACTOR BINDING SITE; INTRON; REPEATSContext (SEQ ID NO: 40):AATTGAACCCAGGCCACCACTGTGAAAGTAAAAAACTTTAGCTACTGAGCTACAGTACTGGGTAGTCTCCATTGTGCTTCCCAGAAGGGCTCTAAAGTAC KTAATTTTGAGCTTGCAAAAGCTTTTAACTACTCAACTTAATTTTTAGAGCTAACTGTGACATGAACCCTAAAATTCCTGTTCCCTTGAAGGCAGAGACCA Celera SNP ID: hCV2720255Public SNP ID: rs10889674 SNP in Genomic Sequence: SEQ ID NO: 14SNP Position Genomic: 95192 SNP Source: dbSNP; CeleraPopulation (Allele,Count): caucasian (G,43|T,77) SNP Type:TRANSCRIPTION FACTOR BINDING SITE; INTRON; REPEATSContext (SEQ ID NO: 41):GTGCAACAGTCAGAATTCTACTTGGAGCCAAACATTAAGTACGTATTTCAAGTGAGATGTCAAGAAACAGGCAAAAGGTACTGGCAGCCTTGGAGTTCAC YGTTTTTTCATAAAACACCTGAAACAGGTGAGTGTACTTATATATTTTATTCTGTTGGGCTTTTCTTTATATATCTTTTCTGCTGAGCACAGTGGCTCACA Celera SNP ID: hCV2990018Public SNP ID: rs7530511 SNP in Genomic Sequence: SEQ ID NO: 14SNP Position Genomic: 63217 SNP Source: dbSNP; Celera; HapMapPopulation (Allele,Count): caucasian (T,15|C,105) SNP Type:MISSENSE MUTATION; HUMAN-MOUSE SYNTENIC REGION Context (SEQ ID NO: 42):ACCCACTGACATCAGATACATGTAGCCCAACTTTCTCAAACAAAAAGTTGTTTCCTGGGGTAGTTGTGCACTCTGGAAAAACAGTCACTCTGTGGCCTAA RGTAAAGGTTAATTTTGCTTCCCCCCACCCTTTCTCCTTTGAGACCTTTGCTTTGAGCAGAGTAAAGAGAATAGTAATTCTGGTATCAAATGAAGACTAAT Celera SNP ID: hCV31222825Public SNP ID: rs10889671 SNP in Genomic Sequence: SEQ ID NO: 14SNP Position Genomic: 83390 SNP Source: dbSNP; HapMapPopulation (Allele,Count): caucasian (A,14|G,106) SNP Type: INTRONContext (SEQ ID NO: 43):ATGACACATGGAATTCTGGGCTAACAGTTGCTTCCATCTCTACAGGGCACCTTACTTCTGGTAAGAAAATACAACTTAGGCTTTTTGAGTAGTCTTTTAG KAATTGCCCATTTTAACCCATCATACTGAAAAAATCACATCAGGTGTTAAGTTTCTGGACAATAAGATATGCCTTATGTCTTCCATAGGAAAATAATAGAC Celera SNP ID: hCV31222838Public SNP ID: rs11465804 SNP in Genomic Sequence: SEQ ID NO: 14SNP Position Genomic: 80190 SNP Source: dbSNP; HapMapPopulation (Allele,Count): caucasian (T,111|G,9) SNP Type: INTRONGene Number: 3 Celera Gene: hCG16057 - 84000313410212 Gene Symbol:FLJ31951 Protein Name: hypothetical protein FLJ31951Celera Genomic Axis: GA_x5YUV32VUFE (16696263..16766296) Chromosome: 5OMIM NUMBER: OMIM Information: Genomic Sequence (SEQ ID NO: 15):SNP Information Context (SEQ ID NO: 44):CATCCTGGGCCACACGCAGCCCAGGAGTTGGACAAGCTTAGTCTACAATTTCAAAGAAGTAACTTGCTGAGGTAACATATTTACTAGGTAAGGAAACAAT YTGTATCAAGTCTGATTCTAAAGTTAATTTTCCTTTCTACTAACCATGCTGCCTACCTAAGTGGAATGAACTAGATTGTGAAAACATGGATTCAAGTTAAA Celera SNP ID: hCV2081970Public SNP ID: rs1897565 SNP in Genomic Sequence: SEQ ID NO: 15SNP Position Genomic: 43496 SNP Source:dbSNP; Celera; HapMap; ABI Val; HGBASE Population (Allele,Count):caucasian (T,93|C,27) SNP Type: INTRON Context (SEQ ID NO: 45):GGTTACTAACAGCACTGAACATTATCAATAAGTATATGAAAACATTTGCAATTATTTGGTGAAATGTTCACATTCTTTGCCCATTTTTCTGCTAGAATAC RTATCCTACTGCCTGATCGAAATAGTAATCCTTAGTCACATGATTGCATTTTTCTAATATGTCCCTTGTCTTAATATTTTAAATAACTTTATTCTCTTATA Celera SNP ID: hCV2081982Public SNP ID: rs10076782 SNP in Genomic Sequence: SEQ ID NO: 15SNP Position Genomic: 30195 SNP Source: dbSNP; Celera; HapMapPopulation (Allele,Count): caucasian (G,93|A,27) SNP Type: INTRONContext (SEQ ID NO: 46):GAGCTAATGCTCCCAGCCCTTATATAGTATCTACATCTTAAGCTTTAATTTTCAAAGAAGAATTTCACTGATATAGATGAGGAATTCATTCAGGCAATCA RTGCTTGTCCCTTACTATGCAGCTCCATGCTTCTATATAGTTATCTTTCCTACACAATTGCTTATTAGTTGGTTTTTATGTGAAAGCATCTAATTTTACAT Celera SNP ID: hCV2081985Public SNP ID: rs2043270 SNP in Genomic Sequence: SEQ ID NO: 15SNP Position Genomic: 23213 SNP Source: dbSNP; Celera; HapMap; ABI_ValPopulation (Allele,Count): caucasian (A,110|G,8) SNP Type: INTRONContext (SEQ ID NO: 47):GCCAAAAATACTATTGACACAAACATGCATCACAACTCACTCTACAGCATTAACCAAACAATCCATAACAAACTAAGTTGACAATGGCAAAGCTGTTAGT KTTTAAATTATACACAGTAATTTGTAATTAAAAAGCAAGACCAGTGGCATTTAAAAATGATGACCTAGGCCAGGTGTAGTAGTGCACACCTATAATCCCAG Celera SNP ID: hCV30377542Public SNP ID: rs6888950 SNP in Genomic Sequence: SEQ ID NO: 15SNP Position Genomic: 49981 SNP Source: dbSNP Population (Allele,Count):caucasian (T,90|G,24) SNP Type: INTRON Gene Number: 4 Celera Gene:hCG2043024 - 208000044135268 Gene Symbol: EBF Protein Name:early B-cell factor Celera Genomic Axis:GA_x5YUV32VUFE (16804013..17223138) Chromosome: 5 OMIM NUMBER: 164343OMIM Information: Genomic Sequence (SEQ ID NO: 16): SNP InformationContext (SEQ ID NO: 48):AAAGATGCAGGGTGTGCTGGGTATTTCTTAATTATTATTATTATTATTATCATATCTGCTAAATCACTCCTCGCTCGACATTTAATACAGGGTGATAGAC MAAACACTGCTGATTTGGGGTTTGCCATAATGACTGACCTGAGCAAAGGCTTTGTGTACAAAGTGGCTCCCCATTAAATAAGGGCTCTGTGGAACCAGCTG Celera SNP ID: hCV1992684Public SNP ID: rs929779 SNP in Genomic Sequence: SEQ ID NO: 16SNP Position Genomic: 370599 SNP Source:dbSNP; Celera; HapMap; ABI_Val; HGBASE Population (Allele,Count):caucasian (A,47|C,73) SNP Type: INTRON Context (SEQ ID NO: 49):AGATTGTGATTCAGATCTGGGATGGGGCTCAGGAACCTGCATTTTAACAATGGAGGTTCTAATGTGGTCATTGGCAGGTTGTTCTAATGTGGGGGCCACA GCCTCTCTCGGAGACAGGCTGTACATGGCCAGCCAGCATTCTGGTAATATGAGCCAAATGCCCATTGACCTAATTTTGGAGAAGAGGTTTATCAACATGTC Celera SNP ID: hDV70663683Public SNP ID: rs17860508 SNP in Genomic Sequence: SEQ ID NO: 16SNP Position Genomic: 303975 SNP Source: CDX Population (Allele,Count):no_pop (G,—|,—) SNP Type: INDEL; PROMOTER Gene Number: 5 Celera Gene:hCG16059 - 84000313412783 Gene Symbol: UBLCP1 Protein Name:hypothetical protein MGC10067 Celera Genomic Axis:GA_x5YUV32VUFE (16618000..16660740) Chromosome: 5 OMIM NUMBER:OMIM Information: Genomic Sequence (SEQ ID NO: 17): SNP InformationContext (SEQ ID NO: 50):CCAATCCCCTACCAATACCAAAGGACAACTGTTACATCATAAGCTCTTATTACTTAACATTCATTATTCATTCATTCAGCATATATTTATTGAGCATCTA YGTTAGCCAATATCATGCTAGGTTCTGAGAATGGAAAGGTGAATAAATTGTCTGTTCCTTAACAAAATTTACTGATATGGTTTGTCTTTGTGTCCCCACCC Celera SNP ID: hCV2081926Public SNP ID: rs11744690 SNP in Genomic Sequence: SEQ ID NO: 17SNP Position Genomic: 6853 SNP Source: dbSNP; Celera; HapMap; ABI_ValPopulation (Allele,Count): caucasian (T,81|C,39) SNP Type:INTERGENIC; UNKNOWN; REPEATS Context (SEQ ID NO: 51):CAAAGGCCCCCTTCCATTTCTCCTCTCCAGAGTGTTCCAGTAAGAACATCCCCTTCTAGCTATTTCACACATGGACAACCAAGAAATAGTCATTTACAGA RCATTTTGCATTTGTACAATTTCACTCGTTATTTCTCCCCCAGTACCTAATGGGGGCTGCAGCGTGTACTCTGTTCGTGGTTAAATTCTGCTGCCAGAAGT Celera SNP ID: hCV2084270Public SNP ID: rs2082412 SNP in Genomic Sequence: SEQ ID NO: 17SNP Position Genomic: 37489 SNP Source: dbSNP; Celera; HGBASEPopulation (Allele,Count): caucasian (G,93|A,27) SNP Type:INTERGENIC; UNKNOWN Context (SEQ ID NO: 52):TCCCATGATGGTCAAGGAATAATTTTGGAGGAGACGTTTAACTTTAAAAAAAAAAATACAATCATTAGTTTCATGTTTGTTTAAAAGAAACTTTGTTTTC STAACCAACATTTGAGCTCCATTCATCTCTTGATGCAGGGAGAGATGTTATTGTAAATGTCTAGTTCTTTATGTTACTTTACAGTAGGGTTTTTAAAAGAC Celera SNP ID: hCV7537756Public SNP ID: rs1368437 SNP in Genomic Sequence: SEQ ID NO: 17SNP Position Genomic: 26679 SNP Source:dbSNP; Celera; HapMap; ABI Val; HGBASE Population (Allele,Count):caucasian (C,112|G,8) SNP Type: INTRON Context (SEQ ID NO: 53):GTCCACCCCCTGACAATGATGATGTTGTTAATGACTTTGATATTGAAGATGAAGTAGTTGAAGTAGAAAATAGGTAAGTGCTTTTCGCTTTAGAAGTAAT SAGTTGTCATGTGAGAACAAGTGAATATTTTATCTAATTATATGTTTTCCATTAGGGAAGAAAACCTACTGAAAATTTCTCGCAGAGTGAAAGAGTACAAA Celera SNP ID: hCV2084255Public SNP ID: rs3734104 SNP in Genomic Sequence: SEQ ID NO: 17SNP Position Genomic: 17181 Related Interrogated SNP:hCV2084293 (Power = .6) Related Interrogated SNP: hCV30449508 (Power =.51) SNP Source: dbSNP; Celera; HapMap; HGBASEPopulation (Allele,Count): caucasian (G,53|C,67) SNP Type: INTRONContext (SEQ ID NO: 54):TGATATATATATATGTGTGTGTGTGTATATATATTTTTTTATTTTTTTGGCCAGAAAGTATTCTAGTTTATGGATGCATCATCATTTAACCATGTTATTC MCTGTTGATTATAAATAATTTTTTATAAGGAATATTTTAAAAATTATGTTTGATATTTTATCACTAGTTTTCTGTTTAGTTATATTCAAGCAGATTCTTGA Celera SNP ID: hCV2084256Public SNP ID: rs4147384 SNP in Genomic Sequence: SEQ ID NO: 17SNP Position Genomic: 17768 Related Interrogated SNP:hCV2084293 (Power = .51) SNP Source: dbSNP; Celera; HapMap; HGBASEPopulation (Allele,Count): caucasian (C,58|A,56) SNP Type: INTRONContext (SEQ ID NO: 55):AGGGAATTGTGGGGTCAGAGCCCCCATACAGAGTCCCTACTGGGGCACTGCCTAGTGGAGCTGAGAGAAGAGGGCCACCACCCTCCAGGCCCCAGAATGG SAGATCTGACAACAGCTTGTACTGTGTGCCTGGAAAATCCACAGACACTCAATGCCAGCCCGTGAAAGCAGCTGGGAGGGAGGGTTTACCTTGCAAAGCCA Celera SNP ID: hCV27883435Public SNP ID: rs4921442 SNP in Genomic Sequence: SEQ ID NO: 17SNP Position Genomic: 13800 Related Interrogated SNP:hCV2084293 (Power = .9) Related Interrogated SNP: hCV30449508 (Power =.8) SNP Source: dbSNP; HapMap Population (Allele,Count):no_pop (C,—|G,—) SNP Type: INTRON; REPEATS Gene Number: 6 Celera Gene:hCG1979566 - 84000313412803 Gene Symbol: Protein Name:Celera Genomic Axis: GA_x5YUV32VUFE (16570845..16613096) Chromosome: 5OMIM NUMBER: OMIM Information: Genomic Sequence (SEQ ID NO: 18):SNP Information Context (SEQ ID NO: 56):CTGTGTGCCCAGCACTTCCTCTGCATGCCTCAGATGCATTTGACAATCTCAGGTGAACTGCACTTCAGGGTCAAGGGAACCCCGGCCATGGTTCTAAGAA RCAACTCCCATTTTAGTATCACCTACATTTGAAACCACAGAGCACTGTCCAGGAGAGGTGATGGTGGTGGGTCTCCTCCTTTGGCTCTCTGGCCCATCAGC Celera SNP ID: hCV1992693Public SNP ID: rs1433048 SNP in Genomic Sequence: SEQ ID NO: 18SNP Position Genomic: 27906 SNP Source:dbSNP; Celera; HapMap; ABI_Val; HGBASE Population (Allele,Count):caucasian (G,21|A,99) SNP Type: INTRON Context (SEQ ID NO: 57):CCACTTCCAACATTGGGGATCAAATTTCAACATGAGATTTGGAGGGACAAATATGCAAACCATATCAGGTGTTGATGGTGAAGGGGTGCTGTGTTTCTTT YTGGGGTATTGAAAATATTCCAGAATTTATTGTGGTGATGGGAGCACAACTCTGTAAGTGTATAAAACCTGTTGAATTAGACACCTTAAAAGAGTCACTTG Celera SNP ID: hCV2084281Public SNP ID: rs7730390 SNP in Genomic Sequence: SEQ ID NO: 18SNP Position Genomic: 2853 SNP Source: dbSNP; Celera; HapMapPopulation (Allele,Count): caucasian (T,91|C,27) SNP Type:INTERGENIC; UNKNOWN; REPEATS Context (SEQ ID NO: 58):AATTACTTAAATATTTAAATAGCATGAAGGCCCATGGCAACTTGAGAGCTGGAAAATCTATACATAAATTAGCTGATTGTTTCAATGAGCATTTAGCATC KAACTATACAAATACAGCAAAGATATCATTGTGATCCTAAAAAAACGTTTTAAAGCAAATCAGATAGAAATTATCTTTTTGGGTCTATTCCGTTGTGTCTT Celera SNP ID: hCV2084293Public SNP ID: rs3212227 SNP in Genomic Sequence: SEQ ID NO: 18SNP Position Genomic: 15011 SNP Source: dbSNP; Celera; HapMap; HGBASEPopulation (Allele,Count): caucasian (T,93|G,27) SNP Type: UTR3; INTRONContext (SEQ ID NO: 59):GGCTTTGTCCAGTGATTTTAAAAGTGGGGTGAAAGGAGTCTGGGGCGGTACAAAAGGGCCTCTGGAACCTTGCAACAGGCAAAGGAATTCTGCTGTAAGG YGAGGAAGCTGGGAAGCCAATATCTTAGCCTCTATAAGTGTAGACATTCTGTTTAGTAAAATAATTTTATAATATCTGGAACAGCCAGGAGCTATCCATTT Celera SNP ID: hCV2084296Public SNP ID: rs2853696 SNP in Genomic Sequence: SEQ ID NO: 18SNP Position Genomic: 16721 SNP Source:dbSNP; Celera; HapMap; ABI_Val; HGBASE Population (Allele,Count):caucasian (T,26|C,94) SNP Type: INTRON Context (SEQ ID NO: 60):CCCCTCTGACTCTCTCTGCAGAGAGTGTAGCAGCTCCGCACGTCACCCCTTGGGGGTCAGAAGAGCTGAAGTCAAAGACAGAAATTAGCCTGTGTTACAC MTTGGGGAGAGAGTTCCTAGTGATTGTAGCCAGTAAGGCAGGTAAGGCCTCAACTGTTGTCTGAGGACACAGTTTCTCCAACTGGGCTGATTTCTACCCAG Celera SNP ID: hCV2084297Public SNP ID: rs919766 SNP in Genomic Sequence: SEQ ID NO: 18SNP Position Genomic: 19625 SNP Source: dbSNP; Celera; HapMap; HGBASEPopulation (Allele,Count): caucasian (A,110|C,10) SNP Type: INTRONContext (SEQ ID NO: 61):GTCTGCTTCAGGGCCCCTAAGATCTACGCCCTGGAGCTCTTGTTTTTATTTTTGACTCAAGGTGCAATTTCAGCAAGTCATTTGTAGCTTTGAATTCTCC KTTTATCCCTTTCTTTGGTGCTATGAGGCTTCAGGAAGCATGGCCAGGCAATTTGGATGAGTGGGTTCAAACACAGCAGAGACTATTCTCAGTTCCCAATA Celera SNP ID: hCV2084298Public SNP ID: rs2853694 SNP in Genomic Sequence: SEQ ID NO: 18SNP Position Genomic: 21149 SNP Source: dbSNP; Celera; HapMap; HGBASEPopulation (Allele,Count): caucasian (G,65|T,55) SNP Type: INTRONContext (SEQ ID NO: 62):TATCTGCCTTACATTTGACTGAGGATTAAATGAAAAAAAAAAAAAGCACGTAAAGTACTTAGCACAGTGTCTGCCACACAGTAAATTCGGTGTTAGTTAT YGTTACTTATAGACTGAGGAGTCAGCCAACTGTACAGAGAAACTCTCTTAACAATTTTCCATGGATATTTAAGGATTTCGTTCCCTCTGTTTTAAATCACC Celera SNP ID: hCV2084301Public SNP ID: rs3213093 SNP in Genomic Sequence: SEQ ID NO: 18SNP Position Genomic: 23040 SNP Source: dbSNP; Celera; HGBASEPopulation (Allele,Count): caucasian (C,93|T,27) SNP Type:INTRON; REPEATS Context (SEQ ID NO: 63):TTCATGGAGCCATATTTTCTGGTCATAATTGTGTATCAGGTTCATTCATGCTAATGAGAAAGGGATTCCAGATTTTCTTTGCATCTGTCTGCTTCTCACA KGGCTGTTAAGAAGCCACCTGCCATTCTGACAATTTCATGTCCTTAGCCATAACTACTTGTCCTCTCTCTTGAATCTTAAGATCTTTTTGCCTTCCAGACA Celera SNP ID: hCV7537839Public SNP ID: rs1368439 SNP in Genomic Sequence: SEQ ID NO: 18SNP Position Genomic: 14075 SNP Source: dbSNP; Celera; HapMap; HGBASE;Population (Allele,Count): caucasian (G,26|T,94) SNP Type: UTR3; INTRONContext (SEQ ID NO: 64):GCAATGCTCAACTGTTTCAGTCAAATACCTTAAAAATGAGCATTCCTGGGTTGGGTGACGGAATATTGACAAATTACAGCTTTGTCAGAACTGCTACTAA STCTAGGCGGACCTTGCTATGTACTTTATTCCCTTATAAAGTTTGTGAGTGGCAGAGACAGGCCTAGAAGTCAAGCCTTCTTGGACACTGCTCAGTGCTGT Celera SNP ID: hCV27471935Public SNP ID: rs3212217 SNP in Genomic Sequence: SEQ ID NO: 18SNP Position Genomic: 27191 SNP Source: dbSNP; HapMap; HGBASEPopulation (Allele,Count): caucasian (G,93|C,27) SNP Type: INTRONContext (SEQ ID NO: 65):GCCTCTCTGAGGAGGTGACATTTAGCTGACACCAAAAGGAAAGATGTCAGTTGTGTTAAGAGCAGAGGGAAGCATATGTGCGAAGCACCTGCTAGGAGCC RTGATCTTTGTGTGGAGCAGTGCCAGGCCTACAGAGCCCAACCACACACCCTAGCATGTCTCTGCCTCCTCTTATCTAGAAGACCTAATTGAGGAAGGAGT Celera SNP ID: hCV32389158Public SNP ID: rs3213102 SNP in Genomic Sequence: SEQ ID NO: 18SNP Position Genomic: 18657 SNP Source: dbSNP; HapMap; HGBASEPopulation (Allele,Count): caucasian (G,116|A,2) SNP Type: INTRONContext (SEQ ID NO: 66):CTCTTATTTTTAAGATGAGAAACTTAAAGCTTAGAGAAGGAATGTGACTTTCTGGATCAACATCTAGCAGTTGTTTATTTAGTGCTTACTACATAAAGAG MACTGGGCTAGAAGCAGTTGAGAGAGAAAAAAAGGGCTTACCTGGATCCCGCTTCCTAGGAGCAAATACTTTTACTCAATAAATATTTATTAAGTCAGTGT Celera SNP ID: hDV68553886Public SNP ID: rs3212220 SNP in Genomic Sequence: SEQ ID NO: 18SNP Position Genomic: 26256 SNP Source: dbSNP; HapMap; ABI_Val; HGBASEPopulation (Allele,Count): caucasian (C,93|A,27) SNP Type: INTRONContext (SEQ ID NO: 67):ATAGCTTTTCATTTTTTAACTGGGGCCAAAGTTAGTTAATCCACAAGAATGGGGATCCCAGCTGTCATTTTGGTTGATATCACAACTGACGACCAAGACC RTCACAAATATGGGAGCAAGTCTGATTTGTAACATTATTATAATTATGAATCCAATTACTTTAAGGAATGCACGAAAGGCTTTTTAAAAATTTCAATAGTA Celera SNP ID: hDV71045748Public SNP ID: rs6894567 SNP in Genomic Sequence: SEQ ID NO: 18SNP Position Genomic: 29029 SNP Source: dbSNP; HapMapPopulation (Allele,Count): caucasian (A,94|G,26) SNP Type: INTRONContext (SEQ ID NO: 68):ACAGACCTAGTTAGACCATAGTCCATATTTCAAATATAATTACATGTGCTCATAGCTGAGAACCTTCTCCTGGGATGGATGCATTTCACCAGGTCACTGC YGAAATGTTGTACTTTTATGGATGGTGATGAGGAAGCATCTGTTTTAGGTGTGGTATTTCCTGGAGGCAGAAAACTGCTTGAGTTAGCTCATTCAGTTTTT Celera SNP ID: hCV31985592Public SNP ID: rs7709212 SNP in Genomic Sequence: SEQ ID NO: 18SNP Position Genomic: 36238 SNP Source: dbSNP; HapMap; ABI ValPopulation (Allele,Count): caucasian (T,76|C,44) SNP Type:INTERGENIC; UNKNOWN Context (SEQ ID NO: 69):ACAGACCTAGTTAGACCATAGTCCATATTTCAAATATAATTACATGTGCTCATAGCTGAGAACCTTCTCCTGGGATGGATGCATTTCACCAGGTCACTGC YGAAATGTTGTACTTTTATGGATGGTGATGAGGAAGCATCTGTTTTAGGTGTGGTATTTCCTGGAGGCAGAAAACTGCTTGAGTTAGCTCATTCAGTTTTT Celera SNP ID: hDV70665567Public SNP ID: rs7709212 SNP in Genomic Sequence: SEQ ID NO: 18SNP Position Genomic: 36238 SNP Source: dbSNP; HapMap; ABI_ValPopulation (Allele,Count): caucasian (T,76|C,44) SNP Type:INTERGENIC; UNKNOWN Context (SEQ ID NO: 70):GCTTCCCAACACTGGTGCCAACATCAGCATGCCAATGACGCTTGGGTGGGCATCTCTTTTTTAAGTTTTGTTTGTTACCTTTAATTAGAAAAGGGAGTTA YTATTAAAGAATAAAAAGATATAGTCACAGATGGCATGGGTGGCACATGCAAAGATCCCTAGTTTGAGGGCCAGGCAGACTTCTAGAACTGTGTGACTCTG Celera SNP ID: hCV3169809Public SNP ID: rs730691 SNP in Genomic Sequence: SEQ ID NO: 18SNP Position Genomic: 28288 Related Interrogated SNP:hCV2084293 (Power = .6) Related Interrogated SNP: hCV30449508 (Power =.6) SNP Source: dbSNP; Celera; HapMap; ABI_Val; HGBASEPopulation (Allele,Count): caucasian (T,47|C,73) SNP Type:TRANSCRIPTION FACTOR BINDING SITE; INTRON Context (SEQ ID NO: 71):GAGAAACTTCCAGCACAATTTCAGTTTCATAGAGAATACGGCAGGGCACAATATTCAGCAGAGTAACATAGTGGTTAAAAGCTCAGGGTGTCGAGAACAA YGAACCAAGACTGTCATCCTGTCTCCACTAACCAGCTGGGGGATTTGGAACAAGGTATTTCATTATCATGAGCCTCAGTTTCCTCATCTGTAAAATGATAA Celera SNP ID: hCV29927086Public SNP ID: rs3213094 SNP in Genomic Sequence: SEQ ID NO: 18SNP Position Genomic: 22830 Related Interrogated SNP:hCV2084293 (Power = .9) Related Interrogated SNP: hCV30449508 (Power =.8) SNP Source: dbSNP; HapMap; HGBASE Population (Allele, Count):caucasian (C,93|T,27) SNP Type: INTRON; REPEATS Gene Number: 7Celera Gene: hCG2038173 - 208000027037926 Gene Symbol: Protein Name:Celera Genomic Axis: GA_x5YUV32VUFE (16437748..16475467) Chromosome: 5OMIM NUMBER: OMIM Information: Genomic Sequence (SEQ ID NO: 19):SNP Information Context (SEQ ID NO: 72):ATCCTCAGAAGTGGGCGGCAGAGAAGGAGGAACGTGCTTGAGTCGCAGTCCCCAAAAAGGGAGGAACTCATTGGCCCAGCTTAGGCCTGGTGTCTGCCTA YCTGTGGTTCAGTCAGCTGTGGTCGGTGGGCAGGACACACCTGAAGGAGCATATCTTGGCTGTGTGGGTTGGGCAGACATCCCACAATGCTCATGTAGGGG Celera SNP ID: hCV28024675Public SNP ID: rs4921230 SNP in Genomic Sequence: SEQ ID NO: 19SNP Position Genomic: 14833 SNP Source: dbSNP; HapMap; HGBASEPopulation (Allele,Count): caucasian (C,88|T,30) SNP Type:DONOR SPLICE SITE Gene Number: 8 Celera Gene: Chr5: 154787857..154898929Gene Symbol: Protein Name: Celera Genomic Axis:GA_x5YUV32VUFE (16472963..16584035) Chromosome: 5 OMIM NUMBER:OMIM Information: Genomic Sequence (SEQ ID NO: 20): SNP InformationContext (SEQ ID NO: 73):TCAAAGCAGAACCTTAGGCTCTAAGGGAAACAAGACAGAAGGATTCTGCTGACAAGACAGTAAAGTAGCCTGCTCATCTGGTGGTAGGCACTGTGTCAGC RTTCTAGGTTGTAAATGTAGGAAGTAAGCAGATCAGAGGTTTGCTCAACAACCTGCCTAGTGAGCCAAACTGCTTGCTCTTGAGGCCATGTAGTCCTTCTG Celera SNP ID: hCV1994965Public SNP ID: rs953861 SNP in Genomic Sequence: SEQ ID NO: 20SNP Position Genomic: 15582 SNP Source: dbSNP; Celera; HapMap; ABI_ValPopulation (Allele,Count): caucasian (G,20|A,100) SNP Type:TRANSCRIPTION FACTOR BINDING SITE; INTERGENIC; UNKNOWNContext (SEQ ID NO: 74):CACTAATATGAGAACAATCTCTTTAGGACTGGAAACCACGAAGTCAATTGAATTGAATGCACCACAACCCAGTGAGTTAAATCTTTGTGGAAAGATTCCA SAAATGCCTCTAAAGTTGCATCTATAAGCTTAATGATCTTATGTCTGTGTCTCCATGGATGCCAAGTGATATGATTTGGATCTCTATCCCCACCCAAATCT Celera SNP ID: hCV1994990Public SNP ID: rs6861600 SNP in Genomic Sequence: SEQ ID NO: 20SNP Position Genomic: 62615 SNP Source: dbSNP; CeleraPopulation (Allele,Count): caucasian (C,82|G,38) SNP Type:INTERGENIC; UNKNOWN Context (SEQ ID NO: 75):CTGACTTGCTTCATACTTCTTCCTGCCTCCGCTAGCCTCCACCCAGGGAAGGTGTGCTTCTCGGTAAGTCAGTTTGAGAGAAGCAGTGTAGTGTAGTGGT SAATAGTCTGGATTTACATCTTTGATCTTCCATTTACTACGCTTGTGACCTAGGGGGTGTTGCTTCCCCTCTCTGTTCCAATTATTTATCCATAAAATAGA Celera SNP ID: hCV1994992Public SNP ID: rs6887695 SNP in Genomic Sequence: SEQ ID NO: 20SNP Position Genomic: 65645 SNP Source: dbSNP; Celera; HapMap; ABI_ValPopulation (Allele,Count): caucasian (G,82|C,38) SNP Type:INTERGENIC; UNKNOWN Context (SEQ ID NO: 76):TAAATTTCCAACTCATGCCTTTTGGGGGACACATTCAAACTATAGCAAATACTAAGTTAAGGAAGTTTCAGCTCTGTCTGGCAGCCTCATAATATTTCAA YGCTTCATCATTTGAATGCTTATTAATTAACCAACTTCCTGTATGCCATGTGATCAGATGTCACAAGAGGAGTTCCTTTGGGATGAACTTAGTTCTTTGTG Celera SNP ID: hCV1995017Public SNP ID: rs4921496 SNP in Genomic Sequence: SEQ ID NO: 20SNP Position Genomic: 91073 SNP Source:dbSNP; Celera; HapMap; ABI_Val; HGBASE Population (Allele,Count):caucasian (T,27|C,93) SNP Type: INTERGENIC; UNKNOWNContext (SEQ ID NO: 77):TGCTTACTAGAGACCAAAATGCCAAGATTTCAACGGGGGCCAGCCACCCTGGTTTCTATTTTGATGTGATTACTTAGTCATTTAAAGTCAGGTTAATGTT SGCCAACAACAGATGGGGTCAGGACACAGGAGTTCTGCAGCTCACTGGAACTGGACAGTCTTTTAGGGCACCCAGCTCACAAGGCCACACCGTGGCCCGCC Celera SNP ID: hCV7538755Public SNP ID: rs918520 SNP in Genomic Sequence: SEQ ID NO: 20SNP Position Genomic: 69310 SNP Source:dbSNP; Celera; HapMap; ABI_Val; HGBASE Population (Allele,Count):caucasian (C,20|G,100) SNP Type:TRANSCRIPTION FACTOR BINDING SITE; INTERGENIC; UNKNOWNContext (SEQ ID NO: 78):TGTTTTAGGATCAAAATAATGAAAAAGAATAGAAACCATTTCAACTCAGAAAATAATTCAAAGATGGGAAAAAGGTGTGTACCAAATTCATTGCTCTAAT YATTTCTGTTCTGATAAAAGGAGTTTACAGCAAAGGAATAACTTTTCTGTGTCTCTGAGGCTTTGGAAAAACAAGGCATCAAGAAGCTTTGGGGTGTGGTG Celera SNP ID: hCV7538761Public SNP ID: rs1422878 SNP in Genomic Sequence: SEQ ID NO: 20SNP Position Genomic: 82217 SNP Source:dbSNP; Celera; HapMap; ABI_Val; HGBASE Population (Allele,Count):caucasian (C,68|T,52) SNP Type: INTERGENIC; UNKNOWNContext (SEQ ID NO: 79):GTGGTCTGAACGTTTATGTCTCCCTAAAATTCATATGTTGAATTCCTAACCCCCAAGGTGAGAGTGTTGGGAGGTGGAGCCTTTTAGTCTCCTGGCTGGG MTTAGTGGCCTGATAACATAGACTCCAGAGAGCTGGCTTATTCCTTCCACTATGTGAGGACACAGCAAGAAGCCGCTGTCTGTGGGGAAACAGAGGCTTAC Celera SNP ID: hCV29349404Public SNP ID: rs7704367 SNP in Genomic Sequence: SEQ ID NO: 20SNP Position Genomic: 64493 SNP Source: dbSNP; HapMapPopulation (Allele,Count): caucasian (A,75|C,37) SNP Type:TRANSCRIPTION FACTOR BINDING SITE; INTERGENIC; UNKNOWN; REPEATSContext (SEQ ID NO: 80):ACAGACCTAGTTAGACCATAGTCCATATTTCAAATATAATTACATGTGCTCATAGCTGAGAACCTTCTCCTGGGATGGATGCATTTCACCAGGTCACTGC YGAAATGTTGTACTTTTATGGATGGTGATGAGGAAGCATCTGTTTTAGGTGTGGTATTTCCTGGAGGCAGAAAACTGCTTGAGTTAGCTCATTCAGTTTTT Celera SNP ID: hCV31985592Public SNP ID: rs7709212 SNP in Genomic Sequence: SEQ ID NO: 20SNP Position Genomic: 7177 SNP Source: dbSNP; HapMap; ABI_ValPopulation (Allele,Count): caucasian (T,76|C,44) SNP Type:INTERGENIC; UNKNOWN Context (SEQ ID NO: 81):ACAGACCTAGTTAGACCATAGTCCATATTTCAAATATAATTACATGTGCTCATAGCTGAGAACCTTCTCCTGGGATGGATGCATTTCACCAGGTCACTGC YGAAATGTTGTACTTTTATGGATGGTGATGAGGAAGCATCTGTTTTAGGTGTGGTATTTCCTGGAGGCAGAAAACTGCTTGAGTTAGCTCATTCAGTTTTT Celera SNP ID: hDV70665567Public SNP ID: rs7709212 SNP in Genomic Sequence: SEQ ID NO: 20SNP Position Genomic: 7177 SNP Source: dbSNP; HapMap; ABI ValPopulation (Allele,Count): caucasian (T,76|C,44) SNP Type:INTERGENIC; UNKNOWN Context (SEQ ID NO: 82):CTTCTTTATTTTCTCAACAATGTTTTGCAGTTCTCAGCATATAACTTTCATTTCTTTTGTTCAATTTATTCCTAAGTATTTAATACTTTTTGGTGCTATT KCAGATGAATTTTCCTATTAATTTTCATATTGGTCATTGCAATTGTATAAAAATACAATTATTTTTGTATATTGATCTTGTTTCATGCAATCTTGCTGTGA Celera SNP ID: hCV1994971Public SNP ID: rs7725339 SNP in Genomic Sequence: SEQ ID NO: 20SNP Position Genomic: 20001 Related Interrogated SNP:hCV1994992 (Power = .8) Related Interrogated SNP: hCV31985592 (Power =.7) Related Interrogated SNP: hCV2084293 (Power = .51)Related Interrogated SNP: hCV30449508 (Power = .51) SNP Source:dbSNP; Celera; HapMap Population (Allele,Count): caucasian (G,73|T,35)SNP Type: INTERGENIC; UNKNOWN; REPEATS Context (SEQ ID NO: 83):TAAATAAAATAAAATAAAGTAGAAAAGAAACAAAAATTATAAGATAGGGACATTAAATGGAGTTAGAAATGAGGCTAATAAATAATGAATATGCTGCACC RTGGAATACTACTCAGCCATAAAACAGAACAAAATAATGGACTTTGCAGCAACTTGGATGGAGCTGGAAGCCATTATCTTAAGTGAAATAATTCACAAATG Celera SNP ID: hCV31985582Public SNP ID: rs6556412 SNP in Genomic Sequence: SEQ ID NO: 20SNP Position Genomic: 30385 Related Interrogated SNP:hCV1994992 (Power = .8) Related Interrogated SNP: hCV2084293 (Power =.51) SNP Source: dbSNP; HapMap Population (Allele,Count):caucasian (G,79|A,39) SNP Type: INTERGENIC; UNKNOWN Gene Number: 9Celera Gene: Chr5: 154681788..154721788 Gene Symbol: Protein Name:Celera Genomic Axis: GA_x5YUV32VUFE (16650104..16690104) Chromosome: 5OMIM NUMBER: OMIM Information: Genomic Sequence (SEQ ID NO: 21):SNP Information Context (SEQ ID NO: 84):CCAATCCCCTACCAATACCAAAGGACAACTGTTACATCATAAGCTCTTATTACTTAACATTCATTATTCATTCATTCAGCATATATTTATTGAGCATCTA YGTTAGCCAATATCATGCTAGGTTCTGAGAATGGAAAGGTGAATAAATTGTCTGTTCCTTAACAAAATTTACTGATATGGTTTGTCTTTGTGTCCCCACCC Celera SNP ID: hCV2081926Public SNP ID: rs11744690 SNP in Genomic Sequence: SEQ ID NO: 21SNP Position Genomic: 36217 SNP Source: dbSNP; Celera; HapMap; ABI_ValPopulation (Allele,Count): caucasian (T,81|C,39) SNP Type:INTERGENIC; UNKNOWN; REPEATS Context (SEQ ID NO: 85):TAAATTGAAACATTATGTGGCCTTTCGTGTGGGTTCTTTAACTTAGGATAGTGTTTTCAAGTTTCATCCATATTGTAGCAAGTATCAGCACTTCATTCCA YTTTATGGCTGGATGACATTCCAATGTATGGGTCTGCCATATTTTGTTCATGCCATTTATCCACTCATGGATATGTAGCTTGTTTCCACTTTCTGCCCATT Celera SNP ID: hDV70267720Public SNP ID: rs7719425 SNP in Genomic Sequence: SEQ ID NO: 21SNP Position Genomic: 20001 SNP Source: dbSNP; HapMapPopulation (Allele,Count): caucasian (T,96|C,24) SNP Type:INTERGENIC; UNKNOWN Gene Number: 10 Celera Gene:Chr5: 154576292..154616292 Gene Symbol: Protein Name:Celera Genomic Axis: GA_x5YUV32VUFE (16755600..16795600) Chromosome: 5OMIM NUMBER: OMIM Information: Genomic Sequence (SEQ ID NO: 22):SNP Information Context (SEQ ID NO: 86):GCTTTCTACCAACAGATGTGCAGGGTATTTTTCCCTCTGCCCTTGTTTGTTCATTAATCCATGGTAGGGGACACCAATGGATGGTCACAGTTATGATTCC YCCCATCAATGTGTTTTGCTTGGTTTTCATAGCATTTTTAATTATTTATTTTTGGAGACAGAGTCTCATTCTGTCACCCAGGCTGGAGTACAGTGGCGTGA Celera SNP ID: hCV3220380Public SNP ID: rs270654 SNP in Genomic Sequence: SEQ ID NO: 22SNP Position Genomic: 20001 SNP Source: dbSNP; HapMapPopulation (Allele,Count): caucasian (T,109|C,11) SNP Type:INTERGENIC; UNKNOWN Context (SEQ ID NO: 87):AGCTTGAAGAGACTAAGAGCAGGCAATCCAAGTCTCCTCCACATGTGGAAACCAAGTCCAGAGACGGAGCAGTAACTGCCCGGCTCCCACGGCTTGTAAT YGCAGAAACAAGCTTTAAGCCGGCTGCCTCCTTCCTCGTTGCTTTTACCATTATTTAATTTGTAGGCTTCACAAAGGCTATATGTGTTGAAATTGGCTAAA Celera SNP ID: hCV3220386Public SNP ID: rs270661 SNP in Genomic Sequence: SEQ ID NO: 22SNP Position Genomic: 15035 SNP Source:dbSNP; Celera; HapMap; ABI_Val; HGBASE Population (Allele,Count):caucasian (C,95|T,25) SNP Type: INTERGENIC; UNKNOWN Gene Number: 11Celera Gene: Chr1: 66009859..66049859 Gene Symbol: Protein Name:Celera Genomic Axis: GA_x5YUV32W802 (15835073..15875073) Chromosome: 1OMIM NUMBER: OMIM Information: Genomic Sequence (SEQ ID NO: 23):SNP Information Context (SEQ ID NO: 88):CAGTGGAAATAAATATTTGATGTTATTTTCAATAAATTGTTACTGGAGTTAAACCTCTTGCTATCCTGACAATTCCTCCCTACATCACCCTCTTTGCAAT RGCAGATGGAAGAATTGGCAATAAATGCAATTCAGCTTGAAGAAAACACCCTAAATATTAGAAACCTGTGAAGAACCACCGGATTGCCTTATCAACTCATT Celera SNP ID: hCV2720238Public SNP ID: rs11209032 SNP in Genomic Sequence: SEQ ID NO: 23SNP Position Genomic: 20001 SNP Source: dbSNP; Celera; HapMapPopulation (Allele,Count): caucasian (G,83|A,37) SNP Type:INTERGENIC; UNKNOWN Context (SEQ ID NO: 89):GGCCTCCCCAGCCATATGGAACTGTAAGTCCATTAAATCTCTTTTTTTTGCAAATTGCCCAGTCTTGGGTATGTCTTTACCAGCAGCGTGAAAATGGACT WATACAGCATTTACCACAGTGTCTGGCTCATAGTAACTGTGGCAGAGCCTGCTAATTGTCCGTTCAACTTCCGTTCTCAAATTCTTACTTCCTAACAGAAC Celera SNP ID: hCV31222786Public SNP ID: rs1857292 SNP in Genomic Sequence: SEQ ID NO: 23SNP Position Genomic: 18231 SNP Source: dbSNP; HapMap; HGBASEPopulation (Allele,Count): caucasian (T,10|A,106) SNP Type:INTERGENIC; UNKNOWN; REPEATS

TABLE 3 Primers Al- Primer 1 Primer 2 Marker leles(Allele-specific Primer) (Allele-specific Primer) Common PrimerhCV2084297 A/C ACTAGGAACTCTCTCCCCAAT CTAGGAACTCTCTCCCCAAGTGTTGTCCCCTCTGACTCTC (SEQ ID NO: 90) (SEQ ID NO: 91) (SEQ ID NO: 92)hCV2084296 C/T GGAATTCTGCTGTAAGGC AAGGAATTCTGCTGTAAGGTCTCCTGGCTGTTCCAGATAT (SEQ ID NO: 93) (SEQ ID NO: 94) (SEQ ID NO: 95)hCV90 A/G TGCAAAAACCTACCCAGTTT TGCAAAAACCTACCCAGTTCTTCATTAGACAACAGAGGAGACAT (SEQ ID NO: 96) (SEQ ID NO: 97) (SEQ ID NO: 98)hCV1992693 A/G CGGCCATGGTTCTAAGAAA CGGCCATGGTTCTAAGAAGGAAATGTGGGCTGAGGGATAG (SEQ ID NO: 99) (SEQ ID NO: 100) (SEQ ID NO: 101)hCV1994965 A/G TTACTTCCTACATTTACAACCTAGAAT ACTTCCTACATTTACAACCTAGAACGAGGAGACCTCAAAGCAGAACCTTA (SEQ ID NO: 102) (SEQ ID NO: 103)(SEQ ID NO: 104) hCV1994992 C/G GATCAAAGATGTAAATCCAGACTATTGGATCAAAGATGTAAATCCAGACTAT CCTCTGTGTTCACACTGATATCAATACCT (SEQ ID NO: 105)TC (SEQ ID NO: 107) (SEQ ID NO: 106) hCV1995017 C/TTGGCAGCCTCATAATATTTCAAC TGGCAGCCTCATAATATTTCAATCACAATGGTGCACAAAGAACTAAGT (SEQ ID NO: 108) (SEQ ID NO: 109)(SEQ ID NO: 110) hCV2081970 C/T AATTAACTTTAGAATCAGACTTGATACAGAAATTAACTTTAGAATCAGACTTGA CAGCCCAGGAGTTGGACAAG (SEQ ID NO: 111) TACAA(SEQ ID NO: 113) (SEQ ID NO: 112) hCV3220380 C/T GATGGTCACAGTTATGATTCCCGATGGTCACAGTTATGATTCCT CCTGGGTGACAGAATGAGACTC (SEQ ID NO: 114)(SEQ ID NO: 115) (SEQ ID NO: 116) hCV3220386 C/T CTCCCACGGCTTGTAATCCTCCCACGGCTTGTAATT GCACATTTAGCCAATTTCAACACATAT (SEQ ID NO: 117)(SEQ ID NO: 118) (SEQ ID NO: 119) hCV7537756 C/G TGGAGCTCAAATGTTGGTTAGTGGAGCTCAAATGTTGGTTAC CCTGTTCTTGCAAGGAGGTGATC (SEQ ID NO: 120)(SEQ ID NO: 121) (SEQ ID NO: 122) hCV7538755 C/GTTAGTCATTTAAAGTCAGGTTAATGTTC TAGTCATTTAAAGTCAGGTTAATGT CGGTGTGGCCTTGTGAG(SEQ ID NO: 123) TG (SEQ ID NO: 125) (SEQ ID NO: 124) hCV7538761 C/TGTGTACCAAATTCATTGCTCTAATC GTGTACCAAATTCATTGCTCTAATTCAAAGCTTCTTGATGCCTTGTTT (SEQ ID NO: 126) (SEQ ID NO: 127)(SEQ ID NO: 128) hDV68553886 G/T ACTGCTTCTAGCCCAGTG ACTGCTTCTAGCCCAGTTACCTAAGGCAAGCCATCTGATAC (SEQ ID NO: 129) (SEQ ID NO: 130)(SEQ ID NO: 131) hDV70663683 C/T CTGTCTCCGAGAGAGGG TCTCCGAGAGAGGCTCTAAGGGCTGATGCTTGGAGATTGT (SEQ ID NO: 132) (SEQ ID NO: 133) (SEQ ID NO: 134)hCV28024675 C/T GCTGACTGAACCACAGG AGCTGACTGAACCACAGA GAAGTGGGCGGCAGAGAA(SEQ ID NO: 135) (SEQ ID NO: 136) (SEQ ID NO: 137) hCV2084301 C/TCACAGTAAATTCGGTGTTAGTTATC CACAGTAAATTCGGTGTTAGTTATTTCCACTGGTGATTTAAAACAGA (SEQ ID NO: 138) (SEQ ID NO: 139)(SEQ ID NO: 140) hCV2990018 C/T CAGCCTTGGAGTTCACC GCAGCCTTGGAGTTCACTTGTGCTCAGCAGAAAAGATATAT (SEQ ID NO: 141) (SEQ ID NO: 142)(SEQ ID NO: 143) hCV1994990 C/G GATGCAACTTTAGAGGCATTTGGATGCAACTTTAGAGGCATTTC TTAGGACTGGAAACCACGAAGTCAA (SEQ ID NO: 144)(SEQ ID NO: 145) (SEQ ID NO: 146) hCV2081982 A/G TTCGATCAGGCAGTAGGATATTCGATCAGGCAGTAGGATAC CATTTTGTTGGTTACTAACAGCACTGAA (SEQ ID NO: 147)(SEQ ID NO: 148) (SEQ ID NO: 149) hCV2084270 A/GCAACCAAGAAATAGTCATTTACAGAA CAACCAAGAAATAGTCATTTACAGATCAGGAGCTGGAGGAAACTTCT (SEQ ID NO: 150) AG (SEQ ID NO: 152)(SEQ ID NO: 151) hCV2084281 C/T GGGGTGCTGTGTTTCTTTC GGGGTGCTGTGTTTCTTTTCTAACAGCTGTAATGAGGTATAGTTCAC (SEQ ID NO: 153) (SEQ ID NO: 154) ATACTC(SEQ ID NO: 155) hCV27471935 C/G GCTTTGTCAGAACTGCTACTAACGCTTTGTCAGAACTGCTACTAAG CAGTGTCCAAGAAGGCTTGACTT (SEQ ID NO: 156)(SEQ ID NO: 157) (SEQ ID NO: 158) hCV29349404 A/CAGTCTATGTTATCAGGCCACTAAT GTCTATGTTATCAGGCCACTAAGGATGCTGTGGTCTGAACGTTTATGT (SEQ ID NO: 159) (SEQ ID NO: 160)(SEQ ID NO: 161) hCV30377542 G/T CAATGGCAAAGCTGTTAGTGACAATGGCAAAGCTGTTAGTT GCTGGGATTATAGGTGTGCACTACT (SEQ ID NO: 162)(SEQ ID NO: 163) (SEQ ID NO: 164) hDV70267720 C/T GGAATGTCATCCAGCCATAAAGGGAATGTCATCCAGCCATAAAA GCCTTTCGTGTGGGTTCTTTAACT (SEQ ID NO: 165)(SEQ ID NO: 166) (SEQ ID NO: 167) hDV70665567 C/T TCACCAGGTCACTGCCTTCACCAGGTCACTGCT GCAGTTTTCTGCCTCCAGGAAATAC (SEQ ID NO: 168)(SEQ ID NO: 169) (SEQ ID NO: 170) hDV71045748 A/G AACTGACGACCAAGACCAACTGACGACCAAGACCG TGAGTGGTGCCTGCCTTACTATT (SEQ ID NO: 171)(SEQ ID NO: 172) (SEQ ID NO: 173) hCV1272348 C/T CGAAGTTCTCTGGTCCCGCGAAGTTCTCTGGTCCCA CCGCACCCACTGTCTGATAAAC (SEQ ID NO: 174)(SEQ ID NO: 175) (SEQ ID NO: 176) hCV2720238 A/G CTACATCACCCTCTTTGCAATAACATCACCCTCTTTGCAATG CGGTGGTTCTTCACAGGTTTCTAATA (SEQ ID NO: 177)(SEQ ID NO: 178) (SEQ ID NO: 179) hCV2720255 G/T AGCTTTTGCAAGCTCAAAATTACAGCTTTTGCAAGCTCAAAATTAA CAGGCCACCACTGTGAAAGTAA (SEQ ID NO: 180)(SEQ ID NO: 181) (SEQ ID NO: 182) hCV31222786 A/T ACACTGTGGTAAATGCTGTATTGACACTGTGGTAAATGCTGTATA CCCAGCCATATGGAACTGTAAGT (SEQ ID NO: 183)(SEQ ID NO: 184) (SEQ ID NO: 185) hCV31222825 A/GGGGAAGCAAAATTAACCTTTACT GGGAAGCAAAATTAACCTTTACCCACATTTGCCAGAGATGCACTTCTA (SEQ ID NO: 186) (SEQ ID NO: 187)(SEQ ID NO: 188) hCV31222838 G/T GATGGGTTAAAATGGGCAATTCTGATGGGTTAAAATGGGCAATTA GCTAACAGTTGCTTCCATCTCTACA (SEQ ID NO: 189)(SEQ ID NO: 190) (SEQ ID NO: 191) hCV2084293 G/T CAATGAGCATTTAGCATCGTTCAATGAGCATTTAGCATCT TGGAGGAAAAGTGGAAGATATTA (SEQ ID NO: 192)(SEQ ID NO: 193) (SEQ ID NO: 194) hCV2084298 G/T GCACCAAAGAAAGGGATAAACGCACCAAAGAAAGGGATAAAA CCTCATCGAGTTTTGGAGTCT (SEQ ID NO: 195)(SEQ ID NO: 196) (SEQ ID NO: 197) hCV7537839 G/T CATCTGTCTGCTTCTCACAGCATCTGTCTGCTTCTCACAT GTCTGGAAGGCAAAAAGATC (SEQ ID NO: 198)(SEQ ID NO: 199) (SEQ ID NO: 200)

TABLE 4 Linkage Disequilibrium (LD) SNPs Interrogated ThresholdInterrogated SNP rs LD SNP LD SNP rs Power r² r² hCV1994992 rs6887695hCV11269323 rs11135059 0.51 0.41 0.96248 hCV1994992 rs6887695hCV15894458 rs2546890 0.51 0.41 0.47424 hCV1994992 rs6887695 hCV1994971rs7725339 0.51 0.41 0.9583 hCV1994992 rs6887695 hCV1994990 rs68616000.51 0.41 1 hCV1994992 rs6887695 hCV2084283 rs1549922 0.51 0.41 0.44522hCV1994992 rs6887695 hCV2084298 rs2853694 0.51 0.41 0.43863 hCV1994992rs6887695 hCV27106359 rs12522665 0.51 0.41 1 hCV1994992 rs6887695hCV29349404 rs7704367 0.51 0.41 1 hCV1994992 rs6887695 hCV31985582rs6556412 0.51 0.41 0.96218 hCV1994992 rs6887695 hCV31985592 rs77092120.51 0.41 0.76043 hCV1994992 rs6887695 hDV70665567 rs7709212 0.51 0.410.73433 hCV2084293 rs3212227 hCV11269323 rs11135059 0.51 0.28 0.30508hCV2084293 rs3212227 hCV15803290 rs2421047 0.51 0.28 1 hCV2084293rs3212227 hCV15879826 rs2288831 0.51 0.28 1 hCV2084293 rs3212227hCV15894458 rs2546890 0.51 0.28 0.3318 hCV2084293 rs3212227 hCV1994971rs7725339 0.51 0.28 0.32318 hCV2084293 rs3212227 hCV2081927 rs1942280.51 0.28 0.36701 hCV2084293 rs3212227 hCV2081970 rs1897565 0.51 0.280.43743 hCV2084293 rs3212227 hCV2081982 rs10076782 0.51 0.28 0.43743hCV2084293 rs3212227 hCV2084254 rs2420825 0.51 0.28 0.38665 hCV2084293rs3212227 hCV2084255 rs3734104 0.51 0.28 0.36701 hCV2084293 rs3212227hCV2084256 rs4147384 0.51 0.28 0.29964 hCV2084293 rs3212227 hCV2084261rs1368438 0.51 0.28 0.2808 hCV2084293 rs3212227 hCV2084262 rs176651890.51 0.28 0.36701 hCV2084293 rs3212227 hCV2084270 rs2082412 0.51 0.28 1hCV2084293 rs3212227 hCV2084272 rs2116821 0.51 0.28 0.42118 hCV2084293rs3212227 hCV2084281 rs7730390 0.51 0.28 0.9061 hCV2084293 rs3212227hCV2084283 rs1549922 0.51 0.28 0.31009 hCV2084293 rs3212227 hCV2084298rs2853694 0.51 0.28 0.34311 hCV2084293 rs3212227 hCV2084301 rs32130930.51 0.28 1 hCV2084293 rs3212227 hCV27471935 rs3212217 0.51 0.28 1hCV2084293 rs3212227 hCV27486507 rs3212219 0.51 0.28 1 hCV2084293rs3212227 hCV27508808 rs3212218 0.51 0.28 1 hCV2084293 rs3212227hCV27841092 rs6556405 0.51 0.28 0.43067 hCV2084293 rs3212227 hCV27883435rs7381075 0.51 0.28 0.95073 hCV2084293 rs3212227 hCV29349409 rs68590180.51 0.28 1 hCV2084293 rs3212227 hCV29619986 rs10072923 0.51 0.28 1hCV2084293 rs3212227 hCV29927086 rs3213094 0.51 0.28 1 hCV2084293rs3212227 hCV30377542 rs6888950 0.51 0.28 0.47474 hCV2084293 rs3212227hCV30449508 rs3212220 0.51 0.28 1 hCV2084293 rs3212227 hCV30557642rs10056599 0.51 0.28 1 hCV2084293 rs3212227 hCV3169809 rs730691 0.510.28 0.39264 hCV2084293 rs3212227 hCV31985582 rs6556412 0.51 0.280.30172 hCV2084293 rs3212227 hCV31985592 rs7709212 0.51 0.28 0.45634hCV2084293 rs3212227 hCV32389155 rs3213097 0.51 0.28 1 hCV2084293rs3212227 hCV7537829 rs1433046 0.51 0.28 0.36701 hCV2084293 rs3212227hDV70267720 rs7719425 0.51 0.28 0.52712 hCV2084293 rs3212227 hDV70665567rs7709212 0.51 0.28 0.44187 hCV2084293 rs3212227 hDV70836263 rs170566260.51 0.28 0.31682 hCV2084293 rs3212227 hDV71045748 rs6894567 0.51 0.280.95272 hCV2084293 rs3212227 hDV71102154 rs1473247 0.51 0.28 0.43743hCV2990018 rs7530511 hCV16078411 rs2863212 0.51 0.5 0.77778 hCV2990018rs7530511 hCV2720226 rs2863209 0.51 0.5 0.50884 hCV2990018 rs7530511hCV27868367 rs4655530 0.51 0.5 0.92453 hCV2990018 rs7530511 hCV27868368rs4655693 0.51 0.5 1 hCV2990018 rs7530511 hCV27952715 rs4655692 0.51 0.50.54175 hCV2990018 rs7530511 hCV2990003 rs11804284 0.51 0.5 0.79053hCV2990018 rs7530511 hCV2990015 rs7528924 0.51 0.5 0.54286 hCV2990018rs7530511 hCV31222825 rs10889671 0.51 0.5 0.92453 hCV31222786 rs1857292hCV2720226 rs2863209 0.51 0.895 1 hCV31222825 rs10889671 hCV16078411rs2863212 0.51 0.555 0.69003 hCV31222825 rs10889671 hCV27868367rs4655530 0.51 0.555 1 hCV31222825 rs10889671 hCV27868368 rs4655693 0.510.555 0.92453 hCV31222825 rs10889671 hCV2990003 rs11804284 0.51 0.5550.85817 hCV31222825 rs10889671 hCV2990018 rs7530511 0.51 0.555 0.92453hCV31985592 rs7709212 hCV1994971 rs7725339 0.51 0.48 0.80424 hDV68553886rs3212220 hCV1994971 rs7725339 0.51 0.31 0.32318 hDV68553886 rs3212220hCV2084255 rs3734104 0.51 0.31 0.36701 hDV68553886 rs3212220 hCV2084281rs7730390 0.51 0.31 0.9061 hDV68553886 rs3212220 hCV2084293 rs32122270.51 0.31 1 hDV68553886 rs3212220 hCV27883435 rs7381075 0.51 0.310.95073 hDV68553886 rs3212220 hCV29927086 rs3213094 0.51 0.31 1hDV68553886 rs3212220 hCV3169809 rs730691 0.51 0.31 0.39264 hDV68553886rs3212220 hCV31985592 rs7709212 0.51 0.31 0.45634 hDV68553886 rs3212220hDV70267720 rs7719425 0.51 0.31 0.52712 hDV68553886 rs3212220hDV71045748 rs6894567 0.51 0.31 0.95272

TABLE 5 IL12B and IL23R region SNPs that are significant in a combinedanalysis across all three sample sets (odds ratio 95% confidenceintervals don't cross 1). hCV dbSNP ID Gene Position & AllelesOR_(common) (95% CI) P_(comb) hCV3220386 rs270661 A158492732G  1.21(1.06-1.38) NC hCV3220380 rs270654 G158497687A  1.20 (1.01-1.42) 0.160hCV2081982 rs10076782 FLJ31951 A158537541G  0.80 (0.71-0.90) 0.002hCV2081970 rs1897565 FLJ31951 C158550843T  0.79 (0.70-0.89) 0.001hCV30377542 rs6888950 FLJ31951 G158557329T  0.79 (0.70-0.89) 0.001hDV70267720 rs7719425 C158603516T  0.77 (0.68-0.88) 0.001 hCV7537756rs1368437 UBLCP1 G158639557C  1.19 (1.01-1.41) NC hCV2084270 rs2082412A158650367G  0.65 (0.57-0.75) 9.52E−09 hCV2084281 rs7730390 C158663370T 0.64 (0.56-0.74) 2.45E−09 hCV7537839 rs1368439 IL12B G158674592T  1.24(1.09-1.42) 0.007 hCV2084293 rs3212227 IL12B C158675528A  0.64(0.56-0.73) 1.66E−09 hCV2084296 rs2853696 IL12B A158677238G  1.23(1.08-1.40) 0.011 hCV2084297 rs919766 IL12B C158680142A  1.18(1.00-1.39) 0.173 hCV2084298 rs2853694 IL12B A158681666C  0.83(0.75-0.92) 0.004 hCV2084301 rs3213093 IL12B A158683557G  0.65(0.57-0.75) 6.51E−09 hDV68553886 rs3212220 IL12B T158686773G  0.65(0.56-0.74) 4.22E−09 hCV27471935 rs3212217 IL12B C158687708G  0.65(0.57-0.75) 4.82E−09 hCV1992693 rs1433048 IL12B G158688423A  1.22(1.07-1.39) 0.020 hDV71045748 rs6894567 IL12B G158689546A  0.66(0.58-0.76) 2.33E−08 hDV70663683 rs17860508 IL12B GC158692778TTAGA  0.89(0.81-0.99) 0.120 hDV70665567 rs7709212^(h) C158696755T  0.73(0.65-0.82) 9.24E−07 hCV1994965 rs953861^(h) G158705160A  1.27(1.11-1.45) 0.004 hCV1994990 rs6861600 G158752193C  0.70 (0.62-0.79)4.00E−08 hCV29349404 rs7704367 C158754071A  0.71 (0.63-0.80) 1.10E−07hCV1994992 rs6887695 C158755223G  0.71 (0.63-0.79) 7.02E−08 hCV7538755rs918520 C158758888G  1.29 (1.14-1.46) 0.001 hCV7538761 rs1422878A158771795G  0.82 (0.73-0.91) 0.001 hCV1995017 rs4921496 T158780649C 1.22 (1.09-1.38) 0.004 hCV28024675 rs4921230 T158812974C  0.81(0.72-0.92) 0.010 hCV1272348 rs6687620 IL23R T67421048C 0.834(0.704-0.983) 0.146 hCV2990018 rs7530511 IL23R T67457975C 0.772(0.66-0.913) 0.012 hCV31222838 rs11465804 IL23R G67475114T 0.628(0.502-0.795) 0.00076 hCV31222825 rs10889671 IL23R A67478314G 0.778(0.662-0.919) 0.016 hCV1272298 rs11209026 IL23R A67478546G 0.621(0.494-0.777) 0.00039 hCV2720255 rs10889674 IL23R G67490116T 1.169(1.053-1.298) 0.031 hCV31222786 rs1857292 T67510910A 0.777 (0.665-0.917)0.020 hCV2720238 rs11209032 A67512680G  1.16 (1.037-1.296) 0.057

TABLE 6 Minor allele frequencies and allele-based association ofIL12B-associated SNPs with psoriasis¹. Discovery Sample Set ReplicationSample Set 1 Frequency in Frequency in SNP Cases Controls Allelic CasesControls Number dbSNP ID Gene Type (n = 465) (n = 460) OR P-value² (n =494) (n = 495) 1 rs929779 EBF intron 0.465 0.452 1.05 0.608 0.489 0.4902 rs1422668 EBF intron 0.311 0.324 0.94 0.582 0.344 0.336 3 rs6898290EBF intron 0.415 0.413 1.01 0.925 0.452 0.442 4 rs2161357 intergenic0.194 0.215 0.88 0.273 0.217 0.198 5 rs6897374 intergenic 0.089 0.0741.22 0.236 0.084 0.102 6 rs6896438 intergenic 0.391 0.400 0.97 0.7390.398 0.393 7 rs270661 intergenic 0.197 0.200 0.98 0.907 0.235 0.183 8rs270654 intergenic 0.109 0.103 1.07 0.705 0.136 0.109 9 rs6556398intergenic 0.321 0.304 1.08 0.451 0.295 0.320 10 rs717925 intergenic0.014 0.015 0.91 0.848 0.010 0.028 11 rs10035989 FLJ31951 UTR 0.1990.189 1.06 0.597 0.193 0.185 12 rs2043270 FLJ31951 intron 0.088 0.0751.19 0.310 0.081 0.071 13 rs10076782 FLJ31951 intron 0.214 0.271 0.730.005 0.244 0.283 14 rs1897565 FLJ31951 intron 0.212 0.273 0.71 0.0020.243 0.280 15 rs6888950 FLJ31951 intron 0.213 0.273 0.72 0.003 0.2410.279 16 rs7721001 intergenic 0.417 0.466 0.82 0.035 0.479 0.469 17rs7719425 intergenic 0.157 0.204 0.73 0.011 0.186 0.229 18 rs11744690intergenic 0.344 0.329 1.07 0.520 0.323 0.305 19 rs1368437 UBLCP1 intron0.118 0.094 1.30 0.096 0.129 0.101 20 rs2082412 intergenic 0.145 0.2070.65 4.77E−04 0.143 0.222 21 rs7730390 intergenic 0.144 0.210 0.632.39E−04 0.142 0.224 22 rs3181225 intergenic 0.188 0.177 1.08 0.5460.174 0.178 23 rs1368439 IL12B UTR 0.204 0.191 1.08 0.519 0.227 0.183 24rs3212227 IL12B UTR 0.143 0.209 0.63 2.38E−04 0.139 0.224 25 rs3213120IL12B UTR 0.030 0.027 1.12 0.781 0.031 0.040 26 rs3213119 IL12B F298V0.031 0.027 1.15 0.679 0.030 0.039 27 rs2853696 IL12B intron 0.205 0.1931.08 0.521 0.228 0.183 28 rs3213102 IL12B intron 0.026 0.027 0.95 0.8860.014 0.019 29 rs919766 IL12B intron 0.119 0.099 1.23 0.179 0.135 0.10730 rs2853694 IL12B intron 0.449 0.482 0.87 0.160 0.450 0.508 31rs3213096 IL12B I33V 0.000 0.003 NC 0.123 0.002 0.000 32 rs3213093 IL12Bintron 0.143 0.207 0.64 3.63E−04 0.143 0.223 33 rs2569253 IL12B intron0.531 0.499 1.14 0.177 0.494 0.485 34 rs3212220 IL12B intron 0.144 0.2070.65 4.76E−04 0.143 0.226 35 rs3212217 IL12B intron 0.145 0.208 0.653.90E−04 0.141 0.224 36 rs2546892 IL12B intron 0.161 0.156 1.04 0.7990.149 0.148 37 rs1433048 IL12B intron 0.213 0.185 1.19 0.145 0.212 0.18938 rs6894567 IL12B intron 0.154 0.208 0.69 0.002 0.134 0.217 39rs17860508 IL12B promotor 0.449 0.482 0.87 0.160 0.469 0.514 40rs7709212 intergenic 0.274 0.336 0.75 0.005 0.268 0.351 41 rs953861intergenic 0.189 0.170 1.14 0.276 0.221 0.181 42 rs6869411 intergenic0.390 0.388 1.01 0.962 0.355 0.338 43 rs1833754 intergenic 0.042 0.0520.79 0.323 0.047 0.061 44 rs6861600 intergenic 0.255 0.321 0.72 0.0020.242 0.331 45 rs7704367 intergenic 0.256 0.321 0.73 0.002 0.245 0.33046 rs6887695 intergenic 0.255 0.319 0.73 0.002 0.243 0.332 47 rs918520intergenic 0.253 0.203 1.33 0.011 0.247 0.217 48 rs4921226 intergenic0.317 0.340 0.90 0.296 0.363 0.330 49 rs1422878 intergenic 0.355 0.3690.94 0.529 0.298 0.373 50 rs4921496 intergenic 0.275 0.249 1.14 0.2240.293 0.265 51 rs4921230 intergenic 0.228 0.253 0.88 0.231 0.210 0.260Replication Sample Set 2 Replication Sample Set 1 Frequency in CombinedAnalysis SNP Allelic Cases Controls Allelic OR_(common) Number ORP-value² (n = 481) (n = 424) OR P-value² (95% CI)³ P_(comb) ⁴ 1 1.000.964 0.513 0.471 1.18 0.082 1.07 (0.96-1.19) 0.414 2 1.03 0.740 0.3390.331 1.03 0.765 1.00 (0.90-1.12) NC 3 1.04 0.651 0.444 0.458 0.95 0.5701.00 (0.90-1.11) NC 4 1.12 0.318 0.191 0.196 0.97 0.811 0.99 (0.87-1.13)NC 5 0.80 0.163 0.085 0.103 0.81 0.197 0.91 (0.76-1.09) NC 6 1.02 0.8540.382 0.413 0.88 0.179 0.95 (0.86-1.06) NC 7 1.37 0.005 0.213 0.173 1.300.032 1.21 (1.06-1.38) NC 8 1.29 0.074 0.111 0.092 1.23 0.187 1.20(1.01-1.42) 0.160 9 0.89 0.223 0.299 0.307 0.97 0.759 0.97 (0.87-1.09)NC 10 0.35 0.005 0.020 0.016 1.20 0.725 0.71 (0.47-1.06) NC 11 1.060.646 0.180 0.187 0.96 0.761 1.03 (0.90-1.17) NC 12 1.16 0.397 0.0770.070 1.10 0.653 1.15 (0.95-1.40) 0.538 13 0.82 0.058 0.252 0.285 0.840.123 0.80 (0.71-0.90) 0.002 14 0.83 0.065 0.248 0.284 0.84 0.099 0.79(0.70-0.89) 0.001 15 0.82 0.057 0.248 0.283 0.84 0.098 0.79 (0.70-0.89)0.001 16 1.04 0.685 0.460 0.466 0.98 0.814 0.94 (0.85-1.05) NC 17 0.770.023 0.205 0.242 0.81 0.062 0.77 (0.68-0.88) 0.001 18 1.09 0.410 0.3350.326 1.04 0.689 1.07 (0.95-1.19) 0.699 19 1.31 0.057 0.105 0.107 0.980.939 1.19 (1.01-1.41) NC 20 0.59 6.43E−06 0.166 0.215 0.73 0.010 0.65(0.57-0.75) 9.52E−09 21 0.57 2.89E−06 0.167 0.215 0.73 0.010 0.64(0.56-0.74) 2.45E−09 22 0.97 0.859 0.166 0.170 0.97 0.850 1.01(0.88-1.16) NC 23 1.31 0.016 0.219 0.174 1.33 0.018 1.24 (1.09-1.42)0.007 24 0.56 1.38E−06 0.167 0.214 0.74 0.014 0.64 (0.56-0.73) 1.66E−0925 0.76 0.273 0.026 0.036 0.73 0.273 0.84 (0.62-1.14) NC 26 0.76 0.3270.027 0.035 0.76 0.342 0.86 (0.64-1.17) NC 27 1.31 0.016 0.217 0.1751.31 0.029 1.23 (1.08-1.40) 0.011 28 0.73 0.387 0.024 0.035 0.67 0.1630.78 (0.55-1.10) 0.450 29 1.30 0.062 0.113 0.113 1.00 1.000 1.18(1.00-1.39) 0.173 30 0.79 0.010 0.447 0.494 0.83 0.048 0.83 (0.75-0.92)0.004 31 NC 0.248 0.000 0.000 NC NC NC NC 32 0.58 3.95E−06 0.167 0.2120.74 0.014 0.65 (0.57-0.75) 6.51E−09 33 0.96 0.686 0.500 0.498 1.010.925 1.06 (0.95-1.18) NC 34 0.57 1.89E−06 0.167 0.212 0.74 0.014 0.65(0.56-0.74) 4.22E−09 35 0.57 1.62E−06 0.169 0.212 0.76 0.023 0.65(0.57-0.75) 4.82E−09 36 1.00 1.000 0.140 0.147 0.95 0.688 1.00(0.86-1.16) NC 37 1.15 0.216 0.228 0.181 1.33 0.017 1.22 (1.07-1.39)0.020 38 0.56 1.11E−06 0.165 0.205 0.76 0.029 0.66 (0.58-0.76) 2.33E−0839 0.83 0.043 0.469 0.472 0.99 0.925 0.89 (0.81-0.99) 0.120 40 0.688.07E−05 0.276 0.332 0.77 0.010 0.73 (0.65-0.82) 9.24E−07 41 1.28 0.0290.202 0.154 1.40 0.008 1.27 (1.11-1.45) 0.004 42 1.08 0.447 0.372 0.3740.99 0.922 1.03 (0.92-1.15) NC 43 0.76 0.194 0.059 0.055 1.08 0.762 0.87(0.69-1.10) NC 44 0.64 1.16E−05 0.249 0.306 0.75 0.007 0.70 (0.62-0.79)4.00E−08 45 0.66 2.90E−05 0.251 0.308 0.75 0.007 0.71 (0.63-0.80)1.10E−07 46 0.65 1.49E−05 0.251 0.308 0.75 0.007 0.71 (0.83-0.79)7.02E−08 47 1.19 0.122 0.255 0.200 1.37 0.005 1.29 (1.14-1.46) 0.001 481.15 0.137 0.347 0.355 0.96 0.693 1.00 (0.90-1.12) NC 49 0.71 3.54E−040.299 0.340 0.83 0.062 0.82 (0.73-0.91) 0.001 50 1.15 0.176 0.294 0.2281.41 0.002 1.22 (1.09-1.38) 0.004 51 0.75 0.009 0.188 0.219 0.82 0.1010.81 (0.72-0.92) 0.010 ¹Data may differ slightly from that reported inCargill et al. 2007 due to lack of DNA for a small number of samples.SNPs are listed in order according to their position in the genomiccontig NT_23133 (Entrez Nucleotide). ²Calculated using Fisher's exacttest. ³Calculated using a Mantel-Haenszel common odds ratio (OR).⁴Calculated using Fisher's combined test. Combined P-values were notcalculated (NC) for SNPs where the ORs for the three samples sets areboth above and below 1.

TABLE 7 Minor allele frequencies and allele-based association ofIL23R-associated SNPs with psoriasis¹. Sample Set 1 Sample Set 2Frequency in Frequency in Position & Cases Controls Allelic Cases hCVMarker Gene Type Alleles² (n = 465) (n = 460) OR P-value³ (n = 498)hCV30135285 rs6685003 C1orf141 intron G67360618T 0.046 0.048 0.95 0.8090.042 hCV3249086 rs11209002 C1orf141 intron T67363049C 0.181 0.195 0.910.434 0.158 hCV1272378 rs4655683 intergenic A67384201G 0.347 0.336 1.050.614 0.387 hCV11728603 rs1884444 IL23R H3Q G67406400T 0.451 0.477 0.900.251 0.503 hCV1272351 rs1358749 IL23R intron A67409858G 0.105 0.1350.75 0.051 0.112 hCV1272348 rs6687620 IL23R intron T67421048C 0.1040.132 0.77 0.066 0.106 hCV31222894 rs11465791 IL23R intron A67422084G0.059 0.066 0.89 0.562 0.060 hCV1272335 rs12401432 IL23R intronG67429519C 0.309 0.298 1.05 0.605 0.346 hCV11283730 rs4655688 IL23Rintron C67430570T 0.105 0.136 0.75 0.043 0.113 hCV31222889 rs12562213IL23R intron T67431357C 0.040 0.040 1.00 0.960 0.045 hCV1272330rs10489630 IL23R intron G67435210T 0.415 0.417 0.99 0.931 0.435hCV8906845 rs1569922 IL23R intron T67437551C 0.417 0.406 1.05 0.6350.433 hCV8906837 rs1321150 IL23R intron T67443012C 0.240 0.263 0.890.259 0.238 hCV1272311 rs790631 IL23R intron C67449510T 0.238 0.263 0.870.206 0.235 hCV2990025 rs790632 IL23R intron C67451510A 0.240 0.261 0.890.294 0.226 hCV30369702 rs7517847 IL23R intron G67454257T 0.449 0.4381.05 0.631 0.446 hCV2990018 rs7530511 IL23R L310P T67457975C 0.104 0.1460.67 0.006 0.100 hCV30279129 rs10489629 IL23R intron G67460937A 0.4570.458 0.99 0.939 0.467 hCV27952715 rs4655692 IL23R intron A67464253G0.216 0.245 0.85 0.143 0.207 hCV1272302 rs2201841 IL23R intronC67466790T 0.328 0.298 1.15 0.173 0.326 hCV31222838 rs11465804 IL23Rintron G67475114T 0.039 0.058 0.66 0.062 0.059 hCV11283754 rs10489628IL23R intron T67476695C 0.421 0.404 1.07 0.457 0.408 hCV2989999rs1343152 IL23R intron C67476920A 0.364 0.355 1.04 0.677 0.386hCV31222826 rs10789229 IL23R intron C67478162T 0.374 0.423 0.81 0.0320.398 hCV31222825 rs10889671 IL23R intron A67478314G 0.102 0.143 0.680.008 0.102 hCV1272298 rs11209026 IL23R Q381R A67478546G 0.043 0.0600.71 0.107 0.059 hCV2720255 rs10889674 IL23R intron G67490116T 0.4380.392 1.21 0.047 0.433 hCV31222811 rs12085634 IL23R intron A67491301T0.104 0.093 1.13 0.433 0.101 hCV8367043 rs1343151 IL23R intronT67491717C 0.320 0.339 0.92 0.381 0.358 hCV8367042 rs1008193 IL23Rintron C67492499G 0.271 0.276 0.97 0.786 0.290 hCV30243123 rs6693831IL23R intron T67493455C 0.246 0.270 0.88 0.241 0.214 hCV261080rs10889675 IL23R intron A67494804C 0.145 0.126 1.18 0.222 0.111hCV31222798 rs11465827 IL23R 3′UTR G67497416T 0.005 0.004 1.24 0.7460.009 hCV11283764 rs10889677 IL23R 3′UTR A67497708C 0.325 0.297 1.140.206 0.327 hCV2720250 rs4655531 intergenic T67500366C 0.145 0.124 1.190.200 0.112 hCV26465573 rs11209030 intergenic A67510363C 0.255 0.2670.94 0.556 0.268 hCV31222786 rs1857292 intergenic T67510910A 0.107 0.1390.74 0.033 0.105 hCV31222784 rs11209031 intergenic C67512176A 0.2600.268 0.96 0.686 0.271 hCV2720238 rs11209032 intergenic A67512680G 0.3390.304 1.18 0.103 0.340 hCV3277193 rs12141431 intergenic C67519611G 0.3000.279 1.11 0.328 0.311 hCV8361864 rs1495965 intergenic G67526096A 0.4480.435 1.05 0.597 0.441 hCV26465558 rs11578380 intergenic G67533953C0.437 0.429 1.03 0.728 0.436 hCV2694787 rs12131065 A67541594G 0.2390.240 0.99 0.947 0.220 hCV11728631 rs2001257 IL12RB2 intron G67550178C0.039 0.038 1.02 0.921 0.039 hCV11728636 rs2066445 IL12RB2 intronA67554563G 0.192 0.188 1.02 0.864 0.172 hCV8367041 rs1495964 IL12RB2intron C67567406T 0.418 0.419 0.99 0.936 0.410 hCV3277169 rs1495963IL12RB2 S238S A67567907G 0.010 0.023 0.42 0.026 0.016 hCV29129912rs6679356 IL12RB2 intron C67592782T 0.182 0.179 1.02 0.859 0.192hCV26465525 rs2307153 IL12RB2 D465G A67606231G 0.016 0.025 0.64 0.1810.017 hCV27517014 rs3790568 IL12RB2 intron A67608648G 0.046 0.049 0.930.734 0.052 hCV22275265 rs4297265 IL12RB2 T643T G67624923A 0.432 0.4231.04 0.701 0.421 hCV12104810 rs1874396 IL12RB2 3′UTR C67635054A 0.0100.025 0.38 0.012 0.014 hCV8366984 rs1109918 IL12RB2 3′UTR C67635070T0.026 0.024 1.08 0.793 0.026 hCV2694742 rs1948526 intergenic T67645934G0.035 0.045 0.77 0.270 0.028 hCV28017716 rs4655707 SERBP1 3′UTRT67648002C 0.433 0.421 1.05 0.623 0.421 hCV2694741 rs4655708 SERBP13′UTR A67648121T 0.432 0.422 1.04 0.666 0.421 hCV2694740 rs1058074SERBP1 3′UTR G67649072A 0.344 0.352 0.97 0.717 0.334 hDV70954502rs17446753 intergenic G67678660C 0.065 0.056 1.18 0.404 0.062 hCV78070rs12138671 intergenic G67690502A 0.473 0.481 0.97 0.718 0.493 hCV8362705rs1340590 intergenic G67696878A 0.290 0.287 1.01 0.898 0.277 hCV8362706rs1340589 intergenic T67696979C 0.175 0.153 1.18 0.193 0.145 Sample Set2 Sample Set 3 Frequency in Frequency in Combined Analysis ControlsAllelic Cases Controls Allelic OR_(common) hCV (n = 498) OR P-value³ (n= 481) (n = 424) OR P-value³ (95% CI)⁴ P_(comb) ⁵ hCV30135285 0.045 0.930.741 0.053 0.043 1.23 0.382 1.035 (0.811-1.334) hCV3249086 0.170 0.920.503 0.171 0.168 1.02 0.900  0.95 (0.823-1.081) hCV1272378 0.338 1.240.025 0.351 0.354 0.98 0.883 1.082 (0.978-1.216) hCV11728603 0.461 1.180.065 0.468 0.473 1.02 0.851 1.009 (0.918-1.131) hCV1272351 0.120 0.930.621 0.109 0.119 0.91 0.506 0.858 (0.725-1.01)  0.218 hCV1272348 0.1210.86 0.321 0.103 0.115 0.88 0.407 0.834 (0.704-0.983) 0.146 hCV312228940.064 0.94 0.779 0.066 0.060 1.11 0.630 0.979 (0.78-1.205)  hCV12723350.302 1.22 0.039 0.318 0.304 1.07 0.510 1.112 (0.998-1.247) 0.181hCV11283730 0.120 0.94 0.674 0.110 0.119 0.92 0.605 0.861 (0.733-1.014)0.232 hCV31222889 0.040 1.13 0.654 0.040 0.046 0.88 0.643 0.992(0.769-1.296) hCV1272330 0.423 1.05 0.616 0.410 0.419 0.97 0.738 1.001(0.901-1.115) hCV8906845 0.425 1.04 0.716 0.413 0.419 0.97 0.812 1.018(0.916-1.132) hCV8906837 0.251 0.93 0.529 0.235 0.246 0.94 0.621 0.919(0.813-1.038) 0.553 hCV1272311 0.251 0.92 0.431 0.235 0.244 0.95 0.7000.913 (0.811-1.031) 0.474 hCV2990025 0.251 0.87 0.224 0.232 0.238 0.970.824 0.913 (0.805-1.023) 0.443 hCV30369702 0.446 1.00 1.000 0.421 0.4340.95 0.568 0.997 (0.899-1.11)  hCV2990018 0.114 0.86 0.345 0.108 0.1310.81 0.146 0.772 (0.66-0.913)  0.012 hCV30279129 0.475 0.97 0.752 0.4480.460 0.95 0.637 0.973 (0.875-1.080) 0.953 hCV27952715 0.213 0.97 0.7820.209 0.222 0.92 0.493  0.91 (0.805-1.037) 0.446 hCV1272302 0.310 1.070.468 0.343 0.319 1.12 0.294 1.113 (0.995-1.243) 0.279 hCV31222838 0.0820.70 0.043 0.036 0.067 0.53 0.004 0.628 (0.502-0.795) 0.00076hCV11283754 0.389 1.08 0.408 0.411 0.392 1.08 0.443 1.078 (0.973-1.200)0.545 hCV2989999 0.395 0.96 0.712 0.380 0.383 0.99 0.923 0.997(0.892-1.111) hCV31222826 0.393 1.02 0.817 0.392 0.398 0.97 0.809 0.931(0.841-1.041) hCV31222825 0.113 0.89 0.467 0.108 0.133 0.79 0.111 0.778(0.662-0.919) 0.016 hCV1272298 0.089 0.64 0.012 0.035 0.066 0.52 0.0030.621 (0.494-0.777) 0.00039 hCV2720255 0.404 1.12 0.217 0.444 0.405 1.170.096 1.169 (1.053-1.298) 0.031 hCV31222811 0.089 1.16 0.357 0.098 0.0861.15 0.416 1.147 (0.959-1.368) 0.483 hCV8367043 0.373 0.94 0.513 0.3270.329 0.99 0.960  0.95 (0.849-1.059) 0.764 hCV8367042 0.278 1.06 0.5830.284 0.257 1.15 0.204 1.057 (0.94-1.193)  hCV30243123 0.225 0.94 0.5860.232 0.265 0.84 0.114 0.882 (0.782-1.001) 0.220 hCV261080 0.108 1.040.829 0.126 0.129 0.97 0.833 1.061 (0.904-1.243) hCV31222798 0.006 1.500.606 0.004 0.007 0.59 0.530 1.052 (0.529-2.244) hCV11283764 0.310 1.080.439 0.342 0.319 1.11 0.318 1.109 (0.991-1.241) 0.312 hCV2720250 0.1081.04 0.829 0.125 0.132 0.94 0.725 1.055 (0.900-1.238) hCV26465573 0.2531.09 0.441 0.289 0.251 1.22 0.071 1.076 (0.951-1.208) hCV31222786 0.1240.83 0.203 0.104 0.130 0.77 0.079 0.777 (0.665-0.917) 0.020 hCV312227840.254 1.09 0.412 0.286 0.252 1.19 0.112 1.076 (0.954-1.212) hCV27202380.305 1.17 0.101 0.357 0.329 1.13 0.216  1.16 (1.037-1.296) 0.057hCV3277193 0.281 1.15 0.166 0.317 0.298 1.10 0.386 1.116 (0.997-1.255)0.259 hCV8361864 0.428 1.06 0.583 0.458 0.461 0.99 0.925 1.031(0.930-1.145) hCV26465558 0.417 1.08 0.387 0.449 0.450 1.00 1.000 1.036(0.935-1.149) 0.865 hCV2694787 0.247 0.86 0.166 0.240 0.236 1.02 0.9120.957 (0.844-1.076) hCV11728631 0.047 0.84 0.439 0.039 0.063 0.59 0.0180.777 (0.607-1.009) hCV11728636 0.178 0.96 0.767 0.171 0.210 0.78 0.036 0.91 (0.799-1.047) hCV8367041 0.413 0.99 0.891 0.400 0.428 0.89 0.2510.955 (0.862-1.067) 0.793 hCV3277169 0.018 0.89 0.733 0.020 0.022 0.880.744 0.713 (0.481-1.079) 0.205 hCV29129912 0.191 1.01 0.954 0.178 0.1940.90 0.431 0.975 (0.853-1.116) hCV26465525 0.027 0.62 0.130 0.020 0.0191.05 1.000 0.746 (0.499-1.069) hCV27517014 0.054 0.96 0.920 0.053 0.0531.00 1.000 0.964 (0.76-1.227)  0.992 hCV22275265 0.439 0.93 0.439 0.4200.441 0.92 0.367  0.96 (0.864-1.064) hCV12104810 0.015 0.92 0.854 0.0220.023 0.93 0.875 0.709 (0.48-1.084)  0.150 hCV8366984 0.036 0.73 0.2450.023 0.026 0.88 0.760 0.879 (0.626-1.189) hCV2694742 0.042 0.67 0.1120.038 0.039 0.97 0.903 0.801 (0.598-1.036) 0.303 hCV28017716 0.439 0.930.439 0.419 0.438 0.93 0.447 0.967 (0.872-1.072) hCV2694741 0.439 0.930.439 0.421 0.440 0.93 0.420 0.964 (0.868-1.071) hCV2694740 0.336 0.990.924 0.329 0.340 0.95 0.618 0.968 (0.868-1.081) 0.938 hDV70954502 0.0780.78 0.158 0.068 0.085 0.79 0.182 0.884 (0.715-1.079) hCV78070 0.4901.01 0.893 0.485 0.491 0.98 0.851 0.985 (0.888-1.094) hCV8362705 0.2960.91 0.370 0.297 0.294 1.01 0.918 0.981 (0.873-1.096) hCV8362706 0.1590.90 0.415 0.147 0.154 0.95 0.693 1.006 (0.864-1.156) ¹Data may differslightly from that reported in Cargill et al. 2007 due to lack of DNAfor a small number of samples. ²DNPs are listed in order according totheir position in the genomic contig NT_032977. The minor allele islisted first, followed by the position in Genome Build 36.2 and then themajor allele. ³Calculated using Fisher's exact test. ⁴Calculated using aMantel-Haenszel common OR. ⁵Calculated using Fisher's combined test.

TABLE 8 Table 1. Demographic, clinical and pooling information¹.Discovery Sample Set² Replication Sample Set 1³ Subphenotype InformationInformation Genetic background White—Utah White—U.S. # Cases 467 498 #Controls 500 498 Female:male gender ratio 1.2 1.2 Average age of onset28 +/− 17 years 29 +/− 15 years Age of onset (<18yrs/18-32yrs/>32yrs)142/169/156 125/171/202 Percent with family history 62.70% 46.60% FamilyHistory (yes/no) 292/175 232/265 Psoriasis worsens after infection(yes/no)⁶  72/376 111/384 % Body surface covered at worst (>13%/<13%)186/281 113/274 Psoriatic arthiritis and psoriasis for at least 10 years(yes/no) 125/239  98/143 Plaque thickness when no treatment (thick/thin)127/110 Thick plaque when no treatment (yes/no) 127/340 319/101 NationalPsoriasis Foundation psoriasis score 124/132/129 — (<7.5/7.5-12.5/>12.5)Inverse Psoriasis (yes/no)  85/382 — Guttate Psoriasis (yes/no)  36/431— Plaque Psoriasis 432 Nail involvment (yes/no) 277/190 — Scalp only(yes/no)  46/421 — PASI score (<10/10-50/>50) — 364/103/4 ¹For pooling,seven subphenotypes were examined in the discovery sample set and sixsubphenotypes were examined in the replication sample set (see below fordetails). Each patient was assessed for each of the relevantsubphenotypes and an aliquot of standardized DNA was added to theappropriate pool. If no information was available for a particularsubphenotype of a patient, that patient's DNA was not included in thatsubphenotype pool. ²For the discovery sample set there were a total of40 pools—20 case pools and 20 control pools (due to DNA availability atthe time the pools were constructed, only 466 cases were included in thepooled case DNAs). The seven subphenotypes studied and the compositionof the case pools are as follows: A) Age of psoriasis onset: Pool 1—ageof onset <18 years, 142 patients; Pool 2—age of onset between 18 and 32years of age, 168 patients; Pool 3—age of onset >32 years, 156 patients;B) Family history of psoriasis (yes/no): Pool 4—yes, 145 patients; Pool5—yes, 146 additional patients; Pool 6—no, 175 patients; C) Psoriasisworsens after infection (yes/no): Pool 7—yes, 72 patients; Pool 8—no,187 patients; Pool 9—no, 188 additional patients; D) Psoriatic arthritis(yes/no): Pool 10—yes, 124 patients; Pool 11—no psoriatic arthritis 10or more years after psoriasis diagnosis,120 patients; Pool 12—nopsoriatic arthritis 10 or more years after psoriasis diagnosis, 119additional patients; E) Plaque thickness without therapy: Pool 13—thickplaques, 126 patients; Pool 14—thin plaques, 110 patients; F) Percentbody surface covered when disease is at its worse: Pool 15—>13%, 185patients; Pool 16—<13%, 141 patients; Pool 17—<13%, 140 additionalpatients; G) Disease severity with treatment as determined by theNational Psoriasis Foundation Psoriasis Score (NSF PS): Pool 18—NPF PS<7.5, 123 patients; Pool 19—NPF PS of 7.5-11.2, 132 patients; Pool20—NPF PS of >11.2, 129 patients. The controls in this sample set werenot individually matched to the cases; consequently, all 500 weredistributed into the same number of pools for each case subphenotypeattempting to approximate the gender ratio found in each case pool. Forexample, the 500 controls were split into three pools for comparisonwith the subphenotype age of onset such that the percentage of femalesin each of the three control pools was similar to that of its “matched”case pool. ³For replication sample set 1 there were a total of 42pools—21 case pools and 21 control pools. The six subphenotypes studiedand the composition of the case pools are as follows: A) Age of onset:Pool 1—<18 years, 125 patients; Pool 2—between 18 and 32 years of age,171 patients; Pool 3—>32 years, 202 patients; B) Family history(yes/no): Pool 4—yes, 232 patients; Pool 5—no, 133 patients; Pool 6—no,132 additional patients; C) Psoriasis worsens after infection (yes/no):Pool 7—yes, 111 patients; Pool 8—no, 192 patients; Pool 9—no, 192additional patients; D) Psoriatic arthritis (yes/no): Pool 10—yes, 98patients, Pool 11—a replicate of pool 10, Pool 12—no psoriatic arthritis10 or more years after psoriasis diagnosis, 143 patients; E) Thickplaque after therapy (yes/no): Pool 13—yes, 160 patients; Pool 14—yes,159 additional patients; Pool 15—no, 101 patients; F) Plaque form ofpsoriasis (yes/no): Pool 16—yes, 216 patients; Pool 17—yes, 216additional patients; G) Cases: Pool 18—249 cases, Pool 19—the remaining249 cases, Pool 20—a replicate of pool 18, Pool 21—a replicate of pool19 (these replicate case-only pools help minimize allele frequencymeasurement error). In this sample set the controls were individuallymatched to the patients; consequently, each control was placed intopools according to the pooling strategy of its matched case.

TABLE 9 Table 2. Allele frequencies and allele-based association of the32 most significant markers with psoriasis using pooled DNAs^(a)Discovery Sample Set Replication Sample Set 1 Cases Controls AllelicCases Controls Allelic dbSNP ID Chromosome Alleles^(b) (n = 466) (n =500) OR P-value (n = 498) (n = 498) OR P-value ss52085987 6 T/G 0.1510.064 2.61 3.13E−10 0.125 0.050 2.71 2.86E−09 rs361525 6 A/G 0.123 0.0462.94 5.25E−10 0.115 0.060 2.04 1.51E−05 rs2280801 6 A/G 0.148 0.066 2.454.20E−09 0.139 0.060 2.54 2.11E−09 rs130076 6 T/C 0.342 0.227 1.752.27E−08 0.364 0.237 1.85 4.84E−10 rs130065 6 T/C 0.338 0.230 1.721.28E−07 0.379 0.259 1.75 9.75E−09 rs8192591 6 A/G 0.106 0.044 2.551.96E−07 0.095 0.045 2.21 1.46E−05 ss52085992 6 T/C 0.195 0.112 1.923.45E−07 0.205 0.117 1.96 7.03E−08 rs1398828 4 G/A 0.367 0.257 1.675.09E−07 0.263 0.254 1.04 0.682 rs3734843 6 A/G 0.140 0.071 2.136.51E−07 0.143 0.065 2.42 7.08E−09 rs2853941 6 A/G 0.502 0.394 1.541.85E−06 0.493 0.371 1.65 4.29E−08 rs1265076 6 T/C 0.440 0.337 1.542.88E−06 0.496 0.348 1.85 2.38E−11 ss48430373 6 G/T  0.248 0.163 1.693.19E−06 0.267 0.167 1.82 6.66E−08 ss52085989 6 T/C 0.080 0.033 2.584.23E−06 0.091 0.037 2.57 9.16E−07 rs1063478 6 T/C 0.150 0.084 1.934.78E−06 0.159 0.102 1.65 2.44E−04 rs1051794 6 A/G 0.332 0.239 1.595.72E−06 0.338 0.265 1.42 4.37E−04 rs2114437 6 C/G 0.101 0.047 2.256.34E−06 0.099 0.047 2.24 9.63E−06 rs1371458 10 G/C  0.096 0.168 0.531.03E−05 0.107 0.126 0.83 0.209 rs2273137 20 C/T  0.065 0.125 0.481.32E−05 0.073 0.086 0.84 0.321 rs432925 16 C/G 0.382 0.286 1.541.52E−05 0.266 0.284 1.09 0.393 rs17107076 14 T/C 0.024 0.002 14.842.03E−05 0.001 0.001 1.07 1.000 rs2523733 6 T/G 0.180 0.112 1.752.14E−05 0.235 0.135 1.96 9.29E−09 rs1576 6 G/C  0.393 0.302 1.492.59E−05 0.438 0.296 1.86 5.04E−11 rs916920 6 G/A 0.240 0.163 1.612.76E−05 0.288 0.178 1.87 6.73E−09 rs15574 6 T/C 0.367 0.278 1.512.87E−05 0.367 0.334 1.16 0.121 rs1535287 6 T/C 0.370 0.469 0.672.97E−05 0.399 0.396 1.01 0.891 ss48423448 6 C/T  0.047 0.015 3.233.24E−05 0.064 0.024 2.84 8.15E−06 rs707936 6 T/C 0.085 0.040 2.224.21E−05 0.081 0.069 1.20 0.306771 rs1046276 16 T/C 0.445 0.354 1.474.50E−05 0.409 0.392 1.08 0.464 ss52085988 6 T/C 0.119 0.066 1.924.78E−05 0.128 0.065 2.11 2.22E−06 rs3212227 5 C/A 0.148 0.219 0.625.03E−05 0.154 0.256 0.53 1.88E−08 rs3096696 6 T/G 0.294 0.214 1.545.24E−05 0.301 0.217 1.55 2.13E−05 ss52085991 6 G/A 0.229 0.310 0.665.43E−05 0.235 0.270 0.83 0.079 ^(a)Alleles are listed according totheir significance (P-values) in the discovery samples. Data arereported for the minor allele. ^(b)The two alleles for the indicated SNP(minor allele/major allele).

TABLE 10 Table 10. Case-control analysis of rs3212227^(a) GenotypicAllelic Association Association Case Control Risk Allele Case AlleleCount (Freq) Count (Freq) OR, 95% CI^(b) P-value^(c) Genotype Count(Freq) Discovery C 133 (0.143) 192 (0.209) CC  10 (0.021) A 799 (0.857)726 (0.791) 1.59 (1.25-2.03) 1.89E−04 CA 113 (0.242) AA 343 (0.736)Replication 1 C 135 (0.137) 219 (0.223) CC  12 (0.024) A 849 (0.863) 763(0.777) 1.81 (1.42-2.28) 7.59E−07 CA 111 (0.226) AA 369 (0.750) Combined1.70 (1.43-2.01) 3.39E−09 Genotypic Association Control Count (Freq)P-value^(d) OR (95% CI)^(b) γ^(e) Discovery  22 (0.047) 148 (0.322) CA vCC 1.68 (0.76-3.69) 1.67 289 (0.630) AA v CC 2.61 (1.22-5.60) 2.57 0.001AA + CA v CC 2.29 (1.07-4.90) 2.27 Replication 1  24 (0.049) 171 (0.348)CA v CC 1.30 (0.62-2.70) 1.29 296 (0.603) AA v CC 2.49 (1.23-5.07) 2.454.17E−06 AA + CA v CC 2.06 (1.02-4.16) 2.03 Combined 8.68E−08 CA v CC1.47 (0.86-2.50) AA v CC 2.55 (1.52-4.28) AA + CA v CC 2.16 (1.29-3.63)^(a)Individual genotyping was performed on 467 cases and 460 controlsfrom the discovery sample set. ^(b)Odds ratios for the discovery andreplication sample sets were determined for the common risk allele usingstandard methods. For the combined analysis, the method ofMantel-Haenszel was used. ^(c)Allelic P-values for the discovery andreplication sample sets were calculated using Fisher's exact test.Allelic combined P-values were determined using Fisher's combined test.^(d)Genotypic P-values were calculated using the Williams-correctedG-test. Genotypic combined P-values were determined using Fisher'scombined test. ^(e)Relative risk (γ) estimates for the indicatedgenotypes were calculated using a Bayesian approach as described in themethods.

TABLE 11 Table 11. Minor allele frequencies and allele-based associationof IL12B-associated SNPs with psoriasis^(a) Replication Discovery SampleSet^(b) Sample Set 1 Frequency in Frequency in Cases Controls AllelicCases dbSNP ID Gene Type Position & Alleles^(c) (n = 467) (n = 460) ORP-value^(d) (n = 498) rs929779 EBF intron T158420776G 0.465 0.452 1.050.608 0.488 rs1422668 EBF intron G158438712A 0.310 0.324 0.94 0.5490.343 rs6898290 EBF intron C158446124T 0.415 0.413 1.01 0.925 0.453rs2161357 intergenic C158469697T 0.193 0.215 0.88 0.273 0.218 rs6897374intergenic A158473778C 0.090 0.074 1.24 0.236 0.084 rs6896438 intergenicC158480454G 0.390 0.400 0.96 0.669 0.398 rs270661 intergenic A158492732G0.197 0.200 0.98 0.907 0.233 rs270654 intergenic G158497687A 0.109 0.1031.07 0.651 0.134 rs6556398 intergenic A158505815C 0.321 0.304 1.08 0.4520.297 rs717925 intergenic C158513109G 0.014 0.015 0.91 0.848 0.010rs10035989 FLJ31951 UTR C158517569T 0.198 0.190 1.06 0.638 0.194rs2043270 FLJ31951 intron G158530559A 0.088 0.075 1.18 0.350 0.081rs1897565 FLJ31951 intron C158550843T 0.212 0.273 0.71 0.002 0.243rs7721001 intergenic T158583392C 0.418 0.466 0.82 0.039 0.477 rs11744690intergenic C158619731T 0.345 0.329 1.07 0.489 0.323 rs1368437 MGC10067intron G158639557C 0.118 0.094 1.29 0.097 0.130 rs3212227 IL12B UTRC158675528A 0.143 0.209 0.63 1.89E−04 0.137 rs3213119 IL12B F298VT158676366G 0.031 0.027 1.15 0.680 0.030 rs3213096 IL12B I33VA158682907G 0.000 0.003 NC 0.122 0.002 rs3212220 IL12B intronT158686773G 0.144 0.207 0.64 3.81E−04 0.141 rs1433048 IL12B intronG158688423A 0.213 0.185 1.19 0.146 0.212 ss52085993^(g) IL12B promoterGC158692778TTAGAG 0.453 0.498 0.83 0.056 0.462 rs7709212^(h) intergenicC158696755T 0.272 0.333 0.75 0.005 0.267 rs953861^(h) intergenicG158705160A 0.190 0.169 1.15 0.250 0.220 rs6869411^(h) intergenicC158714182T 0.390 0.388 1.01 0.962 0.358 rs1833754 intergenicG158751505A 0.042 0.052 0.79 0.323 0.047 rs6887695 intergenicC158755223G 0.254 0.319 0.73 0.002 0.242 rs918520 intergenic C158758888G0.253 0.203 1.33 0.011 0.246 rs4921226 intergenic A158763397G 0.3170.340 0.90 0.320 0.365 rs1422878 intergenic A158771795G 0.355 0.369 0.940.562 0.296 rs4921496 intergenic T158780649C 0.274 0.249 1.14 0.2450.291 Replication Sample Set 1 Replication Sample Set 2 Frequency inFrequency in Combined Analysis Controls Allelic Cases Controls AllelicOR_(common) dbSNP ID (n = 498) OR P-value^(d) (n = 481) (n = 424) ORP-value^(d) (95% CI)^(e) P_(comb) ^(f) rs929779 0.487 1.00 1.000rs1422668 0.337 1.03 0.813 rs6898290 0.441 1.05 0.619 rs2161357 0.1981.13 0.293 rs6897374 0.103 0.80 0.165 rs6896438 0.391 1.03 0.748rs270661 0.183 1.36 0.007 rs270654 0.110 1.27 0.100 rs6556398 0.320 0.900.285 rs717925 0.028 0.35 0.005 rs10035989 0.185 1.06 0.607 rs20432700.071 1.15 0.448 rs1897565 0.280 0.83 0.066 rs7721001 0.470 1.03 0.787rs11744690 0.307 1.08 0.440 rs1368437 0.101 1.33 0.049 rs3212227 0.2230.55 7.59E−07 0.167 0.212 0.74 0.014 0.64 (0.56-0.73) 7.85E−10 rs32131190.039 0.77 0.327 rs3213096 0.000 NC 0.248 rs3212220 0.225 0.56 1.37E−06rs1433048 0.190 1.15 0.239 ss52085993^(g) 0.511 0.82 0.031 rs7709212^(h)0.351 0.68 6.65E−05 rs953861^(h) 0.179 1.30 0.025 rs6869411^(h) 0.3391.09 0.421 rs1833754 0.060 0.78 0.198 rs6887695 0.333 0.64 9.77E−060.251 0.308 0.75 0.007 0.70 (0.63-0.79) 4.08E−08 rs918520 0.216 1.180.123 rs4921226 0.328 1.18 0.094 rs1422878 0.376 0.70 1.73E−04 rs49214960.265 1.14 0.193 ^(a)The data used to calculate these frequencies arefrom individual genotyping. Results are reported for the minor allele.^(b)Individual genotyping was performed on 467 cases and 460 controlsfrom the discovery sample set. ^(c)Positions are according to genomiccontig NT_023133. The minor allele is listed first, followed by theposition in genome build 35.1 and then the major allele. ^(d)P-valueswere calculated using Fisher's exact test. ^(e)Calculated using aMantel-Haenszel common odds ratio. ^(f)Calculated using Fisher'scombined test. ^(g)Putative promoter polymorphism.⁴³ ^(h)SNP is locatedwithin AK097548, a gene model with one mRNA as supporting evidence.

TABLE 12 Table 12. Two marker haplotypes for the IL12B region^(a)Discovery Sample Set Replication Sample Set 1 Global P = 0.001 Global P= 5.13E−08 Case Control Case Control Haplotype rs3212227 rs6887695 (n =467) n = 460 OR P-value (n = 498) (n = 498) OR 1 A G 667 (0.714) 587(0.638) 1.42 3.27E−04 716 (0.719) 631 (0.634) 1.48 2 A C 134 (0.144) 141(0.153) 0.93 0.454 144 (0.145) 143 (0.142) 1.01 3 C C 103 (0.110) 152(0.166) 0.63 5.80E−04  96 (0.096) 188 (0.190) 0.46 4 C G  30 (0.032)  40(0.043) 0.73 0.145  40 (0.040)  34 (0.035) 1.18 Replication Sample Set 2Combined Analysis Replication Sample Set 1 Global P = 0.029 GlobalP_(comb) = 5.94E−10^(b) Global P = 5.13E−08 Case Control OR_(common)Haplotype P-value (n = 481) (n = 424) OR P-value (95% CI)^(c) P_(comb)^(b) 1 1.35E−05 678 (0.705) 548 (0.646) 1.31 6.00E−03 1.40 (1.25-1.57)8.11E−09 2 0.874 124 (0.129) 120 (0.142) 0.90 0.345 0.95 (0.82-1.10)0.680 3 2.56E−09 117 (0.122) 140 (0.165) 0.70 7.00E−03 0.58 (0.50-0.68)5.65E−12 4 0.731  43 (0.045)  40 (0.047) 0.95 0.693 0.94 (0.72-1.24)0.516 ^(a)A pseudo-Gibbs sampling algorithm from the program SNPAnalyzerwas used to estimate haplotype frequencies from unphased data treatingcases and controls separately. The Haplo.Stats package was used to testfor association between haplotypes and disease status. ^(b)Calculatedusing Fisher's combined test. ^(c)Calculated using a Mantel-Haenszelcommon odds ratio.

TABLE 13 Table 6. Three marker IL12B haplotypes including the putativepromotor polymorphism, ss52085993^(a) Discovery Replication Global P =0.007 Global P = 2.4E−06 Haplotype rs3212227 ss52085993^(b) rs6887695Case Control P-value Case Control P-value 1 A T G 495 (0.530) 435(0.473) 0.015 513 (0.515)  464 (0.466) 0.026 2 A C G 172 (0.184) 151(0.164) 0.237 202 (0.203)  168 (0.169) 0.029 3 C C C 103 (0.110) 152(0.165) 0.001 95 (0.095) 187 (0.188) 3.18E−09 4 A C C 125 (0.134) 128(0.139) 0.671 135 (0.136)  130 (0.131) 0.851 5 C C G  25 (0.027)  30(0.033) 0.532 31 (0.031)  26 (0.026) 0.800 6 C T G  9 (0.005)  11(0.012) 0.068 10 (0.010)  8 (0.008) 0.587 7 A T C  9 (0.010)  13 (0.014)0.330 10 (0.010)  13 (0.013) 0.454 ^(a)A pseudo-Gibbs sampling algorithmfrom the program SNPAnalyzer was used to estimate haplotype frequenciesfrom unphased data treating cases and controls separately. TheHaplo.Stats package was used to test for association between haplotypesand disease status. ^(b)C indicates the GC allele; T indicates theTTAGAG allele.

TABLE 14 Table 14. Allele frequencies and allele-based association ofmarkers in IL12B-related genes^(a) Replication Discovery Sample SetSample Set 1 Frequency in Frequency in Cases Controls Allelic CasesLocus dbSNP ID Type Chromosome Position^(b) (n = 467 (n = 460) ORP-value^(c) (n = 498) IL23R rs1884444 H3Q 1 G67345833T 0.451 0.476 0.900.284 IL23R rs7530511 L310P 1 T67397408C 0.103 0.146 0.67 0.006 0.100IL23R rs11209026 Q381R 1 A67417979G 0.044 0.060 0.73 0.142 0.051 IL12RB2rs1109918 3UTR 1 C67574503T 0.026 0.024 1.08 0.882 IL12A rs583911 intron3 G161193092A 0.421 0.449 0.89 0.241 IL12A rs2227314 intron 3T161194756G 0.422 0.448 0.89 0.259 IL12A rs2243131 intron 3 C161194760A0.161 0.139 1.18 0.216 IL12A rs2243149 intergenic 3 T161198414C 0.4190.413 1.02 0.814 IL12A rs2243154 intergenic 3 A161198944G 0.086 0.0801.07 0.737 IL12A rs6771983 intergenic 3 T161221155C 0.007 0.004 1.730.548 IL12A rs2914119 intergenic 3 T161227140C 0.160 0.196 0.78 0.0450.191 IL23A rs2371494 intergenic 12 A55014267G 0.058 0.074 0.77 0.161IL23A rs11171806 S106S 12 A55019798G 0.054 0.073 0.72 0.104 IL12RB1rs401502 G378R 19 G18041413C 0.300 0.313 0.94 0.579 IL12RB1 rs375947T365M 19 G18041451A 0.301 0.313 0.95 0.614 IL12RB1 rs11575926 H156R 19A18049408G 0.176 0.174 1.01 0.951 IL12RB1 rs393548 intergenic 19A18058744T 0.206 0.210 0.97 0.819 Replication Sample Set 1 ReplicationSample Set 2 Frequency in Frequency in Combined Analysis ControlsAllelic Cases Controls Allelic OR_(common) Locus (n = 498) ORP-value^(c) (n = 4) (n = 498) OR P-value^(c) (95% CI)^(d) P_(comb) ^(e)IL23R IL23R 0.114 0.87 0.347 0.108 0.130 0.81 0.166 0.78 (0.66-0.91)0.014 IL23R 0.077 0.65 0.004 0.035 0.066 0.52 0.003 0.63 (0.50-0.79)1.89E−04 IL12RB2 IL12A IL12A IL12A IL12A IL12A IL12A IL12A 0.190 1.010.955 IL23A IL23A IL12RB1 IL12RB1 IL12RB1 IL12RB1 ^(a)The data used tocalculate these frequencies are from individual genotyping. Results arereported for the minor allele. ^(b)The minor allele is listed first,followed by the position in genome build 35.1 and then the major allele.^(c)P-values were calculated using Fisher's exact test. ^(d)Calculatedusing a Mantel-Haenszel common odds ratio. ^(e)Calculated using Fisher'scombined test.

TABLE 15 Table 15. Two marker haplotypes for the IL23R gene^(a)Discovery Sample Set Replication Sample Set 1 Global P = 0.004 Global P= 0.002 Case Control Case Control Haplotype rs7530511 rs11209026 (N =467) (n = 460) OR P-value (n = 498) (n = 498) OR P-value 1 C G 796(0.852)  730 (0.793) 1.50 9.48E−04 840 (0.843)  794 (0.797) 1.37 0.006 2T G 96 (0.103) 135 (0.147) 0.67 0.005 9 (0.098) 113 (0.113) 0.85 0.249 3C A 42 (0.045)  55 (0.060) 0.74 0.134 56 (0.056)   89 (0.089) 0.61 0.0044 T A 0 0 2 (0.002) 0 Replication Sample Set 2 Combined Analysis GlobalP = 0.003 Global P_(comb) = 4.14E−06^(b) Case Control OR_(common)Haplotype (n = 481) (n = 424) OR P-value (95% CI)^(c) P_(comb) ^(b) 1824 (0.857) 682 (0.804) 1.45 0.003 1.44 (1.25-1.65) 3.13E−06 2 104(0.108) 110 (0.130) 0.81 0.003 0.77 (0.65-0.91) 3.22E−04 3  34 (0.035) 56 (0.066) 0.52 0.153 0.62 (0.49-0.77) 0.005 4 0 0 ^(a)A pseudo-Gibbssampling algorithm from the program SNPAnalyzer was used to estimatehaplotype frequencies from unphased genotyping data treating cases andcontrols separately. The Haplo.Stats package was used to test forassociation between haplotypes and disease status. ^(b)Calculated usingFisher's combined test. ^(c)Calculated using a Mantel-Haenszel commonodds ratio.

TABLE 16 Table 16. Diplotype analysis for the IL12B SNPs rs3212227 andrs6887695 Discovery Sample Set Replication Sample Set 1 Global P =0.033^(a) Global P = 3.21E−07^(a) No. of No. of No. of No. of CasesControls Cases Controls Diplotype^(c) (%) (%) OR P-value^(d) (%) (%) ORP-value^(d) A-G/A-G 238 (0.510)  186 (0.403)  1.53 1.54E−03 252 (0.506) 190 (0.382) 1.66 9.81E−05 A-G/C-C 75 (0.161) 96 (0.209) 0.73 0.063 73(0.147) 121 (0.243) 0.54 1.60E−04 A-G/A-C 91 (0.195) 90 (0.196) 0.99 1107 (0.215)  104 (0.209) 1.04 0.877 A-G/C-G 25 (0.054) 29 (0.063) 0.840.576 32 (0.064)  26 (0.052) 1.25 0.499 C-C/C-C  6 (0.013) 13 (0.028)0.45 0.109  4 (0.008)  16 (0.032) 0.24 0.011 C-C/A-C 13 (0.028) 23(0.050) 0.54 0.090  7 (0.014)  27 (0.054) 0.25 6.70E−04 C-C/C-G  3(0.006)  7 (0.015) 0.42 0.221  8 (0.016)  8 (0.016) 1.00 1 A-C/A-C 15(0.032) 14 (0.031) 1.06 1 15 (0.030)  6 (0.012) 2.55 0.075 A-C/C-G 0 0 00 C-G/C-G  1 (0.002)  2 (0.004) 0.49 0.622 0 0 Replication Sample Set 1Global P = 0.042^(a) Combined Analysis No. of No. of Global P =1.13E−07^(b) Cases Controls OR_(common) Diplotype^(c) (%) (%) ORP-value^(d) (95% CI)^(e) P_(comb) ^(f) A-G/A-G 232 (0.482)  171 (0.403) 1.38 0.019 1.52 (1.31-1.77) 6.14E−07 A-G/C-C 84 (0.175) 91 (0.215) 0.770.130 0.67 (0.55-0.81) 1.39E−04 A-G/A-C 99 (0.206) 81 (0.191) 1.10 0.6171.04 (0.87-1.25) A-G/C-G 31 (0.064) 34 (0.080) 0.79 0.370 0.94(0.69-1.27) C-C/C-C  5 (0.010)  8 (0.019) 0.55 0.402 0.38 (0.19-0.68)0.01 C-C/A-C 13 (0.027) 27 (0.064) 0.41 0.009 0.39 (0.25-0.59) 6.51E−05C-C/C-G 10 (0.021)  6 (0.014) 1.48 0.615 0.95 (0.50-1.80) A-C/A-C  6(0.012)  6 (0.014) 0.88 1 1.35 (0.81-2.33) 0.52 A-C/C-G 0 0 C-G/C-G  1(0.002) 0 1 ^(a)Calculated using William's-corrected G-test.^(b)Calculated using Fisher's combined test. ^(c)allele 1 rs3212227 -allele 1 rs6887695/allele 2 rs3212227 - allele 2 rs6887695 ^(d)P-valuescalculated using Fisher's exact test. ^(e)Calculated using aMantel-Haenszel common odds ratio. ^(f)Calculated for diplotypes withthe same effect (risk or protection) in all three sample sets usingFisher's combined test.

TABLE 17 Table 17. Diplotype analysis for the IL23R SNPs rs7530511 andrs11209026 Discovery Sample Set Replication Sample Set 1 Global P =0.033^(a) Global P = 8.47E−06^(a) No. of No. of No. of No. of CasesControls Cases Controls Diplotype^(c) (%) (%) OR P-value^(d) (%) (%) ORP-value^(d) C-G/C-G 340 (0.728)  288 (0.626)  1.60 9.58E−04 357 (0.717) 316 (0.635)  1.46 6.73E−03 C-G/T-G 84 (0.180)  112 (0.243)  0.68 0.02081 (0.163)  90 (0.181)  0.88 0.502 C-G/C-A 33 (0.071)  43 (0.093)  0.740.232 45 (0.090)  72 (0.145)  0.59 0.010 C-G/T-A 0 0 0 0 C-A/C-A 2(0.004) 2 (0.004) 0.98 1 1 (0.002) 5 (0.010) 0.20 0.217 C-A/T-G 4(0.009) 8 (0.017) 0.47 0.260 7 (0.014) 7 (0.014) 1.00 1.000 C-A/T-A 0 02 (0.004) 0 0.499 T-G/T-G 4 (0.009) 7 (0.015) 0.56 0.382 5 (0.010) 8(0.016) 0.62 0.579 T-G/T-A 0 0 0 0 T-A/T-A 0 0 0 0 Replication SampleSet 2 Global P = 0.009^(a) Combined Analysis No. of No. of Global P =5.45E−07^(b) Cases Controls OR_(common) Diplotype^(c) (%) (%) ORP-value^(d) (95% CI)^(e) P_(comb) ^(f) C-G/C-G 350 (0.728)  279 (0.658) 1.39 0.025 1.48 (1.26-1.74) 2.24E−05 C-G/T-G 94 (0.195)  81 (0.191) 1.030.933 0.85 (0.70-1.02) C-G/C-A 30 (0.062)  43 (0.101) 0.59 0.037 0.63(0.49-0.82) 0.005 C-G/T-A 0 0 C-A/C-A 1 (0.002)  2 (0.005) 0.44 0.6020.43 (0.08-1.38) 0.667 C-A/T-G 2 (0.004)  9 (0.021) 0.19 0.029 0.52(0.24-1.00) 0.136 C-A/T-A 0 0 T-G/T-G 4 (0.008) 10 (0.024) 0.35 0.1020.49 (0.23-0.95) 0.270 T-G/T-A 0 0 T-A/T-A 0 0 ^(a)Due to small countsin some of the cells as well as the distribution of these counts, GlobalP-values were obtained by performing a permutation procedure on the dataand generating a log likelihood ratio homogeneity statistic for eachpermutation. The convergence of the statistic to its limitingdistribution was measured by plotting the first two central moments as afunction of the number of replicates. When the number of replicatesreached a number in which the error of the P-value estimates from asubsequent modeling procedure was negligible (less than 1%), a Gammadistribution was fit using maximum likelihood estimates for theparameters. Integration of the resulting Gamma distribution yielded theglobal P-values. ^(b)Calculated using Fisher's combined test. ^(c)Allele1 rs7530511 - allele 1 rs11209026/allele 2 rs7530511 - allele 2rs11209026. ^(d)P-values calculated using Fisher's exact test.^(e)Calculated using a Mantel-Haenszel common odds ratio. ^(f)Calculatedfor diplotypes with the same effect (risk or protection) in all threesample sets using Fisher's combined test.

TABLE 18 Table 18. Two-locus diplotypes for IL12B and IL23R^(a)Discovery Sample Set Replication Sample Set 1 Global P = 6.16E−05^(a)Global P = 9.72E−04^(a) No. of No. of No. of No. of Cases Controls CasesControls Two-locus Diplotype^(c) (%) (%) OR P-value^(d) (%) (%) ORP-value^(d) A-G/A-G-C-G/C-G 175 (0.375)  110 (0.239)  1.91 9.62E−06 174(0.350)  121 (0.243)  1.67 2.98E−04 A-G/A-G-C-G/X 56 (0.120) 69 (0.150)0.77 0.211 68 (0.137) 62 (0.125) 1.11 0.638 A-G/A-G-X/X  7 (0.015)  7(0.015) 0.98 1  9 (0.018)  7 (0.014) 1.29 0.802 A-G/X-C-G/C-G 139(0.298)  145 (0.315)  0.92 0.569 159 (0.320)  160 (0.322)  0.99 1A-G/X-C-G/X 51 (0.109) 61 (0.133) 0.80 0.314 48 (0.097) 80 (0.161) 0.560.003 A-G/X-X/X  1 (0.002)  9 (0.020) 0.11 0.011  5 (0.010) 10 (0.020)0.49 0.298 X/X-C-G/C-G 25 (0.054) 33 (0.072) 0.73 0.279 23 (0.046) 34(0.068) 0.66 0.172 X/X-C-G/X 10 (0.021) 25 (0.054) 0.38 0.009 10 (0.020)20 (0.040) 0.49 0.094 X/X-X/X  3 (0.006)  1 (0.002) 2.97 0.624  1(0.002)  3 (0.006) 0.33 0.624 Replication Sample Set 2 Global P =0.005^(a) Combined Analysis No. of No. of Global P = 7.88E−08^(b) CasesControls OR_(common) Two-locus Diplotype^(c) (%) (%) OR P-value^(d) (95%CI)^(e) P_(comb) ^(f) A-G/A-G-C-G/C-G 168 (0.349)  116 (0.274)  1.431.50E−02 1.66 (1.41-1.95) 1.33E−08 A-G/A-G-C-G/X 59 (0.123) 48 (0.113)1.10 0.681 A-G/A-G-X/X  4 (0.008)  7 (0.017) 0.50 0.364 A-G/X-C-G/C-G154 (0.320)  138 (0.325)  0.98 0.887 0.96 (0.82-1.13) 0.968 A-G/X-C-G/X57 (0.119) 56 (0.132) 0.88 0.547 0.73 (0.58-0.91) 0.019 A-G/X-X/X  3(0.006) 12 (0.028) 0.22 0.016 0.27 (0.11-0.53) 0.003 X/X-C-G/C-G 28(0.058) 25 (0.059) 0.99 1 0.78 (0.57-1.06) 0.415 X/X-C-G/X  8 (0.017) 20(0.047) 0.34 0.011 0.40 (0.25-0.61) 7.42E−04 X/X-X/X 0  2 (0.005) 0.000.219 ^(a)Due to small counts in some of the cells as well as thedistribution of these counts, Global P-values were obtained byperforming a permutation procedure on the data and generating a loglikelihood ratio homogeneity statistic for each permutation. Theconvergence of the statistic to its limiting distribution was measuredby plotting the first two central moments as a function of the number ofreplicates. When the number of replicates reached a number in which theerror of the P-value estimates from a subsequent modeling procedure wasnegligible (less than 1%), a Gamma distribution was fit using maximumlikelihood estimates for the parameters. Integration of the resultingGamma distribution yielded the global P-values. ^(b)Calculated usingFisher's combined test. ^(c)Allele 1 rs3212227 - allele 1rs6887695/allele 2 rs3212227 - allele 2 rs6887695 - allele 1 rs7530511 -allele 1 rs11209026/allele 2 rs7530511 - allele 2 rs11209026. For thisanalysis the three non-risk haplotypes for each gene were combined andtermed X. ^(d)P-values calculated using Fisher's exact test.^(e)Calculated using a Mantel-Haenszel common odds ratio. ^(f)Calculatedfor diplotypes with the same effect (risk or protection) in all threesample sets using Fisher's combined test.

TABLE 19 Genotype frequencies for certain significantly associated SNPsacross sample sets. Discovery Replication Replication Sample Set SampleSet 1 Sample Set 2 Frequency in Frequency in Frequency in Combined Geno-Cases Controls Cases Controls Cases Controls Analysis dbSNP ID Gene TypePosition type (n = 465) (n = 460) (n = 494) (n = 495) (n = 481) (n =424) P_(comb) rs7530511 IL23R L310P 97.357 CC 0.801 0.723 0.810 0.7880.793 0.762 0.012572595 CT 0.190 0.262 0.180 0.196 0.199 0.214 TT 0.0090.015 0.010 0.016 0.008 0.024 rs11465804 IL23R Intron 114.496 TT 0.9240.889 0.887 0.846 0.929 0.871 0.000799398 GT 0.074 0.107 0.109 0.1440.068 0.124 GG 0.002 0.004 0.004 0.010 0.002 0.005 rs10889671 IL23RIntron 117.696 GG 0.805 0.728 0.808 0.788 0.794 0.760 0.01667346 AG0.186 0.259 0.180 0.198 0.195 0.214 AA 0.009 0.013 0.012 0.014 0.0100.026 rs11209026 IL23R Q381R 117.928 GG 0.918 0.885 0.888 0.832 0.9320.873 0.000415864 AG 0.078 0.111 0.105 0.158 0.066 0.122 AA 0.004 0.0040.006 0.010 0.002 0.005 rs10889674 IL23R Intron 129.498 TT 0.316 0.3700.340 0.362 0.318 0.359 0.031240837 GT 0.492 0.475 0.454 0.467 0.4760.472 GG 0.192 0.155 0.206 0.171 0.206 0.169 rs1857292 3′ of 150.292 AA0.795 0.736 0.800 0.762 0.805 0.768 0.019748711 IL23R AT 0.196 0.2480.190 0.228 0.183 0.204 TT 0.009 0.015 0.010 0.010 0.012 0.028

TABLE 20 Effect sizes for haplotypes at rs7530511-rs11209026-rs10889674.Discovery Replication Replication Sample Set Sample Set 1 Sample Set 2Counts Counts Counts Cases Controls Cases Controls Cases ControlsHaplotype (n = 465) (n = 460) OR (n = 494) (n = 495) OR (n = 481) (n =424) OR CGG 404 360 1.22 425 403 1.11 424 344 1.16 CGT 385 376 1.04 407390 1.09 398 338 1.07 TGT 95 133 0.68 93 113 0.81 104 110 0.82 CAT 40 550.72 56 88 0.62 34 56 0.52 Others 0 2 N/A 5 0 N/A 0 0 N/A

TABLE 21 Sample Set Sample Set Sample Set Average 1 Global 2 Global 3Global Combined Marker 1 Marker 2 Marker 3 Stratum AdjustBy PositionP-value P-value P-value P-value hCV27952715 hCV1272302 hCV31222838ALL_ALL NONE 6.75E+07 0.07471465 0.2169627 0.01968886 0.013229hCV1272302 hCV31222838 hCV11283754 ALL_ALL NONE 6.75E+07 0.16560720.2285907 0.02535359 0.030801 hCV31222838 hCV11283754 hCV2989999 ALL_ALLNONE 6.75E+07 0.1010597 0.3166093 0.01700306 0.020002 hCV31222826hCV31222825 hCV1272298 ALL_ALL NONE 6.75E+07 0.007165026 0.021632330.007676498 0.000128 hCV31222825 hCV1272298 hCV2720255 ALL_ALL NONE6.75E+07 0.01472221 0.005748261 0.006339455 6.42E−05 hCV1272298hCV2720255 hCV31222811 ALL_ALL NONE 6.75E+07 0.1534734 0.066960850.01715119 0.008284 hCV8367043 hCV8367042 hCV30243123 ALL_ALL NONE6.75E+07 0.139562 0.1900267 0.007045132 0.008677

TABLE 22 Therapeutic agents that target IL12 or IL23 which are in activeclinical development programs. Highest Dev. Status Company Drug ActionIndication Technology Phase 2 Abbott, under license ABT-874 IL-12antagonist; Inflammation; Multiple Monoclonal antibody, fully Clinicalfrom Cambridge Anti-inflammatory; sclerosis; Psoriasis; human;Subcutaneous Antibody Company IL-23 antagonist Rheumatoid arthritis;formulation Autoimmune disease; Crohns disease Phase 2 Synta STA-5326IL-12 antagonist; Multiple sclerosis; Oral formulation ClinicalPharmaceuticals Anti-inflammatory; Psoriasis; Rheumatoid Corp IL-23antagonist arthritis; Autoimmune disease; Crohns disease Phase 3Centocor Inc CNTO-1275 IL-12 antagonist; Multiple sclerosis; Monoclonalantibody, Clinical Immunomodulator; Psoriasis; Crohns humanized;Anti-inflammatory; disease Subcutaneous IL-23 antagonist formulationReferences for each of the therapeutic agents listed in Table 22:ABT-874: Sandborn W J. “How future tumor necrosis factor antagonists andother compounds will meet the remaining challenges in Crohn's disease”,Rev Gastroenterol Disord. 2004; 4 Suppl 3: S25-33. STA-5326: 1) Burakoffet al., “A phase 1/2A trial of STA 5326, an oral interleukin-12/23inhibitor, in patients with active moderate to severe Crohn's disease”,Inflamm Bowel Dis. 2006 Jul; 12(7): 558-65; 2) Borchardt J K. “Focus onsmall molecule inhibitors for treatment of inflammatory and autoimmuneDiseases”, Drug News Perspect. 2004 Nov; 17(9): 607-14. CNTO-1275: PappK A. “Potential future therapies for psoriasis”, Semin Cutan Med Surg.2005 Mar; 24(1): 58-63.

1. A method for identifying an individual who has an altered risk fordeveloping psoriasis, comprising detecting at least one singlenucleotide polymorphism (SNP) in any one of the nucleotide sequences ofSEQ ID NOS:7-12 and 24-89 in said individual's nucleic acids, whereinthe presence of the SNP is indicative of an altered risk for psoriasisin said individual.
 2. The method of claim 1 in which the altered riskis an increased risk.
 3. The method of claim 1 in which the altered riskis a decreased risk.
 4. The method of claim 1, wherein the SNP isselected from the group consisting of the SNPs set forth in Tables 1-7and 9-21.
 5. The method of claim 1 in which detection is carried out bya process selected from the group consisting of: allele-specific probehybridization, allele-specific primer extension, allele-specificamplification, sequencing, 5′ nuclease digestion, molecular beaconassay, oligonucleotide ligation assay, size analysis, andsingle-stranded conformation polymorphism.
 6. (canceled)
 7. The methodof claim 1, wherein the SNP comprises a SNP haplotype selected from thegroup consisting of: the risk haplotype of rs3212227(A) andrs6887695(G); the protective haplotype of rs3212227(C) and rs6887695(C);and the risk haplotype of rs11209026(G) and rs 7530511(C).
 8. The methodof claim 1, wherein the SNP comprises a SNP diplotype selected from thegroup consisting of: two copies of the risk haplotype rs3212227(A) andrs6887695(G); two copies of the protective haplotype rs3212227(C) andrs6887695(C); and two copies of the risk haplotype rs11209026(G) and rs7530511(C).
 9. The method of claim 2, wherein the SNP comprises thetwo-locus diplotype of rs3212227(A), rs6887695(G), rs11209026(G), and rs7530511(C).
 10. The method of claim 1, wherein the SNP comprises ahaplotype, diplotype, two-locus diplotype, or 3-SNP combination selectedfrom the group consisting of: the haplotypes provided in Tables 12, 13,15, and 20; the diplotypes provided in Tables 16 and 17; two-locusdiplotypes provided in Table 18; and the 3-SNP combinations provided inTable
 21. 11. (canceled)
 12. A method for determining whether a humanwill likely benefit from a drug treatment targeting IL12 or IL23, themethod comprising detecting the presence of a SNP in a nucleotidesequence selected from the group consisting of SEQ ID NOS:7-12 and 24-89in said human's nucleic acids, wherein the presence of the SNP indicatesthat said human is likely to benefit from the drug treatment.
 13. Themethod of claim 12, wherein the human is selected for inclusion in aclinical trial of the drug treatment.
 14. A method of treating a humanwho will likely benefit from a drug treatment targeting IL12 or IL23,the method comprising detecting the presence of a SNP in a nucleotidesequence selected from the group consisting of SEQ ID NOS:7-12 and 24-89in said human's nucleic acids, wherein the presence of the SNP indicatesthat said human is likely to benefit from the drug treatment, andadministering said drug treatment to said human.